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A Short History of the World, by H. G. Wells, is part of the HackerNoon Books Series. You can jump to any chapter in this book here . THE CRUSADES AND THE AGE OF PAPAL DOMINION THE CRUSADES AND THE AGE OF PAPAL DOMINION It is interesting to note that Charlemagne corresponded with the Caliph Haroun-al-Raschid, the Haroun-al-Raschid of the Arabian Nights. It is recorded that Haroun-al-Raschid sent ambassadors from Bagdad—which had now replaced Damascus as the Moslem capital—with a splendid tent, a water clock, an elephant and the keys of the Holy Sepulchre. This latter present was admirably calculated to set the Byzantine Empire and this new Holy Roman Empire by the ears as to which was the proper protector of the Christians in Jerusalem. These presents remind us that while Europe in the ninth century was still a weltering disorder of war and pillage, there flourished a great Arab Empire in Egypt and Mesopotamia, far more civilized than anything Europe could show. Here literature and science still lived; the arts flourished, and the mind of man could move without fear or superstition. And even in Spain and North Africa where the Saracenic dominions were falling into political confusion there was a vigorous intellectual life. Aristotle was read and discussed by these Jews and Arabs during these centuries of European darkness. They guarded the neglected seeds of science and philosophy. North-east of the Caliph’s dominions was a number of Turkish tribes. They had been converted to Islam, and they held the faith much more simply and fiercely than the actively intellectual Arabs and Persians to the south. In the tenth century the Turks were growing strong and vigorous while the Arab power was divided and decaying. The relations of the Turks to the Empire of the Caliphate became very similar to the relations of the Medes to the last Babylonian Empire fourteen centuries before. In the eleventh century a group of Turkish tribes, the Seljuk Turks, came down into Mesopotamia and made the Caliph their nominal ruler but really their captive and tool. They conquered Armenia. Then they struck at the remnants of the Byzantine power in Asia Minor. In 1071 the Byzantine army was utterly smashed at the battle of Melasgird, and the Turks swept forward until not a trace of Byzantine rule remained in Asia. They took the fortress of Nicæa over against Constantinople, and prepared to attempt that city. The Byzantine emperor, Michael VII, was overcome with terror. He was already heavily engaged in warfare with a band of Norman adventurers who had seized Durazzo, and with a fierce Turkish people, the Petschenegs, who were raiding over the Danube. In his extremity he sought help where he could, and it is notable that he did not appeal to the western emperor but to the Pope of Rome as the head of Latin Christendom. He wrote to Pope Gregory VII, and his successor Alexius Comnenus wrote still more urgently to Urban II. CRUSADER TOMBS IN EXETER CATHEDRAL Photo: Mansell This was not a quarter of a century from the rupture of the Latin and Greek churches. That controversy was still vividly alive in men’s minds, and this disaster to Byzantium must have presented itself to the Pope as a supreme opportunity for reasserting the supremacy of the Latin Church over the dissentient Greeks. Moreover this occasion gave the Pope a chance to deal with two other matters that troubled western Christendom very greatly. One was the custom of “private war” which disordered social life, and the other was the superabundant fighting energy of the Low Germans and Christianized Northmen and particularly of the Franks and Normans. A religious war, the Crusade, the War of the Cross, was preached against the Turkish captors of Jerusalem, and a truce to all warfare amongst Christians (1095). The declared object of this war was the recovery of the Holy Sepulchre from the unbelievers. A man called Peter the Hermit carried on a popular propaganda throughout France and Germany on broadly democratic lines. He went clad in a coarse garment, barefooted on an ass, he carried a huge cross and harangued the crowd in street or market-place or church. He denounced the cruelties practised upon the Christian pilgrims by the Turks, and the shame of the Holy Sepulchre being in any but Christian hands. The fruits of centuries of Christian teaching became apparent in the response. A great wave of enthusiasm swept the western world, and popular Christendom discovered itself. VIEW OF CAIRO Photo: Lehnert & Landrock Such a widespread uprising of the common people in relation to a single idea as now occurred was a new thing in the history of our race. There is nothing to parallel it in the previous history of the Roman Empire or of India or China. On a smaller scale, however, there had been similar movements among the Jewish people after their liberation from the Babylonian captivity, and later on Islam was to display a parallel susceptibility to collective feeling. Such movements were certainly connected with the new spirit that had come into life with the development of the missionary- teaching religions. The Hebrew prophets, Jesus and his disciples, Mani, Muhammad, were all exhorters of men’s individual souls. They brought the personal conscience face to face with God. Before that time religion had been much more a business of fetish, of pseudoscience, than of conscience. The old kind of religion turned upon temple, initiated priest and mystical sacrifice, and ruled the common man like a slave by fear. The new kind of religion made a man of him. The preaching of the First Crusade was the first stirring of the common people in European history. It may be too much to call it the birth of modern democracy, but certainly at that time modern democracy stirred. Before very long we shall find it stirring again, and raising the most disturbing social and religious questions. Certainly this first stirring of democracy ended very pitifully and lamentably. Considerable bodies of common people, crowds rather than armies, set out eastward from France and the Rhineland and Central Europe without waiting for leaders or proper equipment to rescue the Holy Sepulchre. This was the “people’s crusade.” Two great mobs blundered into Hungary, mistook the recently converted Magyars for pagans, committed atrocities and were massacred. A third multitude with a similarly confused mind, after a great pogrom of the Jews in the Rhineland, marched eastward, and was also destroyed in Hungary. Two other huge crowds, under the leadership of Peter the Hermit himself, reached Constantinople, crossed the Bosphorus, and were massacred rather than defeated by the Seljuk Turks. So began and ended this first movement of the European people, as people. Next year (1097) the real fighting forces crossed the Bosphorus. Essentially they were Norman in leadership and spirit. They stormed Nicæa, marched by much the same route as Alexander had followed fourteen centuries before, to Antioch. The siege of Antioch kept them a year, and in June 1099 they invested Jerusalem. It was stormed after a month’s siege. The slaughter was terrible. Men riding on horseback were splashed by the blood in the streets. At nightfall on July 15th the Crusaders had fought their way into the Church of the Holy Sepulchre and overcome all opposition there: blood-stained, weary and “sobbing from excess of joy” they knelt down in prayer. THE HORSES OF S. MARK, VENICE Originally on the arch of Trajan at Constantinople, the Doge Dandalo V took them after the Fourth Crusade, to Venice, whence Napoleon I removed them to Paris, but in 1815 they were returned to Venice. During the Great War of 1914-18 they were hidden away for fear of air raids. Photo: D. McLeish Immediately the hostility of Latin and Greek broke out again. The Crusaders were the servants of the Latin Church, and the Greek patriarch of Jerusalem found himself in a far worse case under the triumphant Latins than under the Turks. The Crusaders discovered themselves between Byzantine and Turk and fighting both. Much of Asia Minor was recovered by the Byzantine Empire, and the Latin princes were left, a buffer between Turk and Greek, with Jerusalem and a few small principalities, of which Edessa was one of the chief, in Syria. Their grip even on these possessions was precarious, and in 1144 Edessa fell to the Moslim, leading to an ineffective Second Crusade, which failed to recover Edessa but saved Antioch from a similar fate. In 1169 the forces of Islam were rallied under a Kurdish adventurer named Saladin who had made himself master of Egypt. He preached a Holy War against the Christians, recaptured Jerusalem in 1187, and so provoked the Third Crusade. This failed to recover Jerusalem. In the Fourth Crusade (1202-4) the Latin Church turned frankly upon the Greek Empire, and there was not even a pretence of fighting the Turks. It started from Venice and in 1204 it stormed Constantinople. The great rising trading city of Venice was the leader in this adventure, and most of the coasts and islands of the Byzantine Empire were annexed by the Venetians. A “Latin” emperor (Baldwin of Flanders) was set up in Constantinople and the Latin and Greek Church were declared to be reunited. The Latin emperors ruled in Constantinople from 1204 to 1261 when the Greek world shook itself free again from Roman predominance. The twelfth century then and the opening of the thirteenth was the age of papal ascendancy just as the eleventh was the age of the ascendancy of the Seljuk Turks and the tenth the age of the Northmen. A united Christendom under the rule of the Pope came nearer to being a working reality than it ever was before or after that time. A COURTYARD IN THE ALHAMBRA Photo: Lehnert & Landrock In those centuries a simple Christian faith was real and widespread over great areas of Europe. Rome itself had passed through some dark and discreditable phases; few writers can be found to excuse the lives of Popes John XI and John XII in the tenth century; they were abominable creatures; but the heart and body of Latin Christendom had remained earnest and simple; the generality of the common priests and monks and nuns had lived exemplary and faithful lives. Upon the wealth of confidence such lives created rested the power of the church. Among the great Popes of the past had been Gregory the Great, Gregory I (590-604) and Leo III (795-816) who invited Charlemagne to be Cæsar and crowned him in spite of himself. Towards the close of the eleventh century there arose a great clerical statesman, Hildebrand, who ended his life as Pope Gregory VII (1073- 1085). Next but one after him came Urban II (1087-1099), the Pope of the First Crusade. These two were the founders of this period of papal greatness during which the Popes lorded it over the Emperors. From Bulgaria to Ireland and from Norway to Sicily and Jerusalem the Pope was supreme. Gregory VII obliged the Emperor Henry IV to come in penitence to him at Canossa and to await forgiveness for three days and nights in the courtyard of the castle, clad in sackcloth and barefooted to the snow. In 1176 at Venice the Emperor Frederick (Frederick Barbarossa), knelt to Pope Alexander III and swore fealty to him. The great power of the church in the beginning of the eleventh century lay in the wills and consciences of men. It failed to retain the moral prestige on which its power was based. In the opening decades of the fourteenth century it was discovered that the power of the Pope had evaporated. What was it that destroyed the naive confidence of the common people of Christendom in the church so that they would no longer rally to its appeal and serve its purposes? The first trouble was certainly the accumulation of wealth by the church. The church never died, and there was a frequent disposition on the part of dying childless people to leave lands to the church. Penitent sinners were exhorted to do so. Accordingly in many European countries as much as a fourth of the land became church property. The appetite for property grows with what it feeds upon. Already in the thirteenth century it was being said everywhere that the priests were not good men, that they were always hunting for money and legacies. The kings and princes disliked this alienation of property very greatly. In the place of feudal lords capable of military support, they found their land supporting abbeys and monks and nuns. And these lands were really under foreign dominion. Even before the time of Pope Gregory VII there had been a struggle between the princes and the papacy over the question of “investitures,” the question that is of who should appoint the bishops. If that power rested with the Pope and not the King, then the latter lost control not only of the consciences of his subjects but of a considerable part of his dominions. For also the clergy claimed exemption from taxation. They paid their taxes to Rome. And not only that, but the church also claimed the right to levy a tax of one-tenth upon the property of the layman in addition to the taxes he paid his prince. The history of nearly every country in Latin Christendom tells of the same phase in the eleventh century, a phase of struggle between monarch and Pope on the issue of investitures and generally it tells of a victory for the Pope. He claimed to be able to excommunicate the prince, to absolve his subjects from their allegiance to him, to recognize a successor. He claimed to be able to put a nation under an interdict, and then nearly all priestly functions ceased except the sacraments of baptism, confirmation and penance; the priests could neither hold the ordinary services, marry people, nor bury the dead. With these two weapons it was possible for the twelfth century Popes to curb the most recalcitrant princes and overawe the most restive peoples. These were enormous powers, and enormous powers are only to be used on extraordinary occasions. The Popes used them at last with a frequency that staled their effect. Within thirty years at the end of the twelfth century we find Scotland, France and England in turn under an interdict. And also the Popes could not resist the temptation to preach crusades against offending princes—until the crusading spirit was extinct. It is possible that if the Church of Rome had struggled simply against the princes and had had a care to keep its hold upon the general mind, it might have achieved a permanent dominion over all Christendom. But the high claims of the Pope were reflected as arrogance in the conduct of the clergy. Before the eleventh century the Roman priests could marry; they had close ties with the people among whom they lived; they were indeed a part of the people. Gregory VII made them celibates; he cut the priests off from too great an intimacy with the laymen in order to bind them more closely to Rome, but indeed he opened a fissure between the church and the commonalty. The church had its own law courts. Cases involving not merely priests but monks, students, crusaders, widows, orphans and the helpless were reserved for the clerical courts, and so were all matters relating to wills, marriages and oaths and all cases of sorcery, heresy and blasphemy. Whenever the layman found himself in conflict with the priest he had to go to a clerical court. The obligations of peace and war fell upon his shoulders alone and left the priest free. It is no great wonder that jealousy and hatred of the priests grew up in the Christian world. Never did Rome seem to realize that its power was in the consciences of common men. It fought against religious enthusiasm, which should have been its ally, and it forced doctrinal orthodoxy upon honest doubt and aberrant opinion. When the church interfered in matters of morality it had the common man with it, but not when it interfered in matters of doctrine. When in the south of France Waldo taught a return to the simplicity of Jesus in faith and life, Innocent III preached a crusade against the Waldenses, Waldo’s followers, and permitted them to be suppressed with fire, sword, rape and the most abominable cruelties. When again St. Francis of Assisi (1181-1226) taught the imitation of Christ and a life of poverty and service, his followers, the Franciscans, were persecuted, scourged, imprisoned and dispersed. In 1318 four of them were burnt alive at Marseilles. On the other hand the fiercely orthodox order of the Dominicans, founded by St. Dominic (1170-1221) was strongly supported by Innocent III, who with its assistance set up an organization, the Inquisition, for the hunting of heresy and the affliction of free thought. So it was that the church by excessive claims, by unrighteous privileges, and by an irrational intolerance destroyed that free faith of the common man which was the final source of all its power. The story of its decline tells of no adequate foemen from without but continually of decay from within. About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. H. G. Wells (2011). A Short History of the World. Urbana, Illinois: Project Gutenberg. Retrieved October 2022, from https://www.gutenberg.org/files/11870/11870-h/11870-h.htm This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org , located at https://www.gutenberg.org/policy/license.html .

THE CRUSADES AND THE AGE OF PAPAL DOMINION

Healthcare Tech, Healthcare - October 10, 2024 - HackerNoon, the independent technology publishing platform, is pleased to announce the nominees for its annual “Startups of The Year” award, featuring the most promising and innovative companies in the Healthcare Tech startup ecosystem. The list, compiled by the HackerNoon team, showcases 30 startups making significant contributions and driving innovation in the Healthcare Tech industry. https://youtu.be/XOpG6B4AKPU?embedable=true Nominations are open - learn more here. You can nominate your company as the Startup of The Year in the Healthcare Tech industry here. The Startups in the Healthcare Tech industry nominated for Startups of The Year 2024 include: QUALITY HEALTHCARE CENTER
Your Cue
GUTolution
LonVi
HealingHand Tech
BioLinker
FoundationDx
Wassor Womanity
Lumena MindGym
Caare Foundation
Dimple Bindra
Mercurial AI
AGPHARM BIOINNOVATIONS LLP
PetPal PH
Secured Health
MedGos
Aicury
Biocell
Kobeesco
BetterSpace
Wuru
VitruvianMD
Glooma
STMPatch
CARÁ Health
Vuala
Forte Biotech - RAPID Aquaculture Diagnostics
Tenang AI
Alithea Bio
Mindcafe India “We are thrilled to recognize the top startups in the Healthcare Tech industry and celebrate their achievements,” said David Smooke, HackerNoon Founder & CEO. “Starting and sustaining a company is difficult. We’re honored to showcase their work and impact.” Why It Pays Off to Be Nominated Startups of The Year 2024 is a 100% community-driven voting event recognizing startups transforming technology and the world for the better. Starting October 1st, 2024, the internet can nominate and vote for the best ventures in their city and industry. People can nominate their favorite startups until October 31st, and vote once per day per award for who they think should be Startup of The Year up to March 31st, 2025. Winners will be announced in April 2025, after a comprehensive evaluation by the HackerNoon team. Apart from validation from the world's greenest tech publication, all nominees get free interviews relevant to their region and industry. They will also receive curated startup-friendly packages and a free version of their very own Evergreen Tech Company News Page on HackerNoon. In sum, being nominated gives you major credibility as one of the top startups globally and an expert in your industry, meaning even without winning, you still gain tons of exposure. Nominations are open - learn more here. You can nominate your company as the Startup of The Year in the Healthcare Tech industry here. About HackerNoon’s Startups of The Year Startups of The Year 2024 is HackerNoon’s flagship community-driven event celebrating startups, technology, and the spirit of innovation. Currently in its third iteration, the prestigious Internet award recognizes and celebrates tech startups of all shapes and sizes. This year, over 150,000 entities across 4200+ cities, 6 continents, and 100+ industries will participate in a bid to be crowned the best startup of the year! Millions of votes have been cast over the past few years, and many stories have been written about these daring and rising startups. The winners will get a free interview on HackerNoon and an Evergreen Tech Company News page. Visit our FAQ page to learn more. Download our design assets here. Check out the Startups of The Year Merch Shop here. HackerNoon’s Startups of The Year is a branding opportunity unlike any other. Whether your goal is brand awareness or lead generation, HackerNoon has curated startup-friendly packages to solve your marketing challenges. Meet our sponsors: Wellfound: Join the #1 global, startup-focused community. At Wellfound, we're not just a job board—we're the place where top startup talent and the world's most exciting companies connect to build the future. Notion: Notion is trusted and loved by thousands of startups as their connected workspace—from building product roadmaps to tracking fundraising. Try Notion with unlimited AI, FREE for up to 6 months, to build and scale your company with one powerful tool. Get your offer now! Hubspot: If you’re looking for a smart CRM platform that meets the needs of small businesses, look no further than HubSpot. Seamlessly connect your data, teams and customers in one easy-to-use scalable platform that grows with your business. Get started for free. Bright Data: Startups that leverage public web data can make faster, data-driven decisions, giving them a competitive edge. With Bright Data’s scalable web data collection, businesses can grow from a small operation to an enterprise by harnessing insights at every stage. Algolia: Algolia NeuralSearch is the world's only AI end-to-end Search and Discovery Platform combining powerful keyword and natural language processing in a single API. Healthcare Tech, Healthcare - October 10, 2024 - HackerNoon, the independent technology publishing platform, is pleased to announce the nominees for its annual “Startups of The Year” award, featuring the most promising and innovative companies in the Healthcare Tech startup ecosystem. Healthcare Tech, Healthcare - October 10, 2024 - HackerNoon, the independent technology publishing platform, is pleased to announce the nominees for its annual “Startups of The Year” award, featuring the most promising and innovative companies in the Healthcare Tech startup ecosystem. Healthcare Tech , Healthcare Healthcare Tech - October 10, 2024 - October 10, 2024 - HackerNoon, the independent technology publishing platform, is pleased to announce the nominees for its annual “ Startups of The Year ” award, featuring the most promising and innovative companies in the Startups of The Year startup ecosystem. The list, compiled by the HackerNoon team, showcases 30 startups making significant contributions and driving innovation in the Healthcare Tech industry. https://youtu.be/XOpG6B4AKPU?embedable=true https://youtu.be/XOpG6B4AKPU?embedable=true Nominations are open - learn more here. You can nominate your company as the Startup of The Year in the Healthcare Tech industry here. Nominations are open - learn more here . You can nominate your company as the Startup of The Year in the Healthcare Tech industry here . here Startup of The Year in Startup of The Year in the Healthcare Tech industry here here The Startups in the Healthcare Tech industry nominated for Startups of The Year 2024 include: Startups in Startups in Startups in the Healthcare Tech industry the Healthcare Tech industry nominated QUALITY HEALTHCARE CENTER Your Cue GUTolution LonVi HealingHand Tech BioLinker FoundationDx Wassor Womanity Lumena MindGym Caare Foundation Dimple Bindra Mercurial AI AGPHARM BIOINNOVATIONS LLP PetPal PH Secured Health MedGos Aicury Biocell Kobeesco BetterSpace Wuru VitruvianMD Glooma STMPatch CARÁ Health Vuala Forte Biotech - RAPID Aquaculture Diagnostics Tenang AI Alithea Bio Mindcafe India QUALITY HEALTHCARE CENTER Your Cue GUTolution LonVi HealingHand Tech BioLinker FoundationDx Wassor Womanity Lumena MindGym Caare Foundation Dimple Bindra Mercurial AI AGPHARM BIOINNOVATIONS LLP PetPal PH Secured Health MedGos Aicury Biocell Kobeesco BetterSpace Wuru VitruvianMD Glooma STMPatch CARÁ Health Vuala Forte Biotech - RAPID Aquaculture Diagnostics Tenang AI Alithea Bio Mindcafe India “We are thrilled to recognize the top startups in the Healthcare Tech industry and celebrate their achievements,” said David Smooke, HackerNoon Founder & CEO. “Starting and sustaining a company is difficult. We’re honored to showcase their work and impact.” “We are thrilled to recognize the top startups in the Healthcare Tech industry and celebrate their achievements,” said David Smooke, HackerNoon Founder & CEO. “Starting and sustaining a company is difficult. We’re honored to showcase their work and impact.” “We are thrilled to recognize the top startups in the Healthcare Tech industry and celebrate their achievements,” said David Smooke, HackerNoon Founder & CEO . “Starting and sustaining a company is difficult. We’re honored to showcase their work and impact.” David Smooke, HackerNoon Founder & CEO Why It Pays Off to Be Nominated Startups of The Year 2024 is a 100% community-driven voting event recognizing startups transforming technology and the world for the better. Starting October 1st, 2024, the internet can nominate and vote for the best ventures in their city and industry. People can nominate their favorite startups until October 31st , and vote once per day per award for who they think should be Startup of The Year up to March 31st, 2025. Winners will be announced in April 2025, after a comprehensive evaluation by the HackerNoon team. Startups of The Year 2024 nominate their favorite startups until October 31st nominate their favorite startups Apart from validation from the world's greenest tech publication, all nominees get free interviews relevant to their region and industry. They will also receive curated startup-friendly packages and a free version of their very own Evergreen Tech Company News Page on HackerNoon. get free interviews startup-friendly packages startup-friendly packages Evergreen Tech Company News Evergreen Tech Company News In sum, being nominated gives you major credibility as one of the top startups globally and an expert in your industry, meaning even without winning, you still gain tons of exposure. Nominations are open - learn more here. You can nominate your company as the Startup of The Year in the Healthcare Tech industry here. Nominations are open - learn more here . You can nominate your company as the Startup of The Year in the Healthcare Tech industry here . here Startup of The Year in Startup of The Year in the Healthcare Tech industry here here About HackerNoon’s Startups of The Year Startups of The Year 2024 is HackerNoon’s flagship community-driven event celebrating startups, technology, and the spirit of innovation. Currently in its third iteration, the prestigious Internet award recognizes and celebrates tech startups of all shapes and sizes. This year, over 150,000 entities across 4200+ cities, 6 continents, and 100+ industries will participate in a bid to be crowned the best startup of the year! Millions of votes have been cast over the past few years, and many stories have been written about these daring and rising startups. Startups of The Year 2024 Startups of The Year 2024 many stories many stories The winners will get a free interview on HackerNoon and an Evergreen Tech Company News page. free interview on HackerNoon Evergreen Tech Company News Evergreen Tech Company News Visit our FAQ page to learn more. FAQ FAQ Download our design assets here . here here Check out the Startups of The Year Merch Shop here . here here HackerNoon’s Startups of The Year is a branding opportunity unlike any other. Whether your goal is brand awareness or lead generation, HackerNoon has curated startup-friendly packages to solve your marketing challenges. startup-friendly packages startup-friendly packages Meet our sponsors: Meet our sponsors: Meet our sponsors: Wellfound: Join the #1 global, startup-focused community . At Wellfound, we're not just a job board—we're the place where top startup talent and the world's most exciting companies connect to build the future. Wellfound: Join the #1 global, startup-focused community Join the #1 global, startup-focused community Notion: Notion is trusted and loved by thousands of startups as their connected workspace—from building product roadmaps to tracking fundraising. Try Notion with unlimited AI, FREE for up to 6 months , to build and scale your company with one powerful tool. Get your offer now ! Notion: Try Notion with unlimited AI, FREE for up to 6 months Get your offer now ! Hubspot: If you’re looking for a smart CRM platform that meets the needs of small businesses, look no further than HubSpot. Seamlessly connect your data, teams and customers in one easy-to-use scalable platform that grows with your business. Get started for free . Hubspot: Get started for free Get started for free Bright Data: Startups that leverage public web data can make faster, data-driven decisions, giving them a competitive edge. With Bright Data’s scalable web data collection , businesses can grow from a small operation to an enterprise by harnessing insights at every stage. Bright Data: Bright Data’s scalable web data collection Bright Data’s scalable web data collection Algolia: Algolia NeuralSearch is the world's only AI end-to-end Search and Discovery Platform combining powerful keyword and natural language processing in a single API. Algolia: AI end-to-end Search and Discovery Platform AI end-to-end Search and Discovery Platform

Startups of the Year 2024: 5,508 Startups Nominated in the Healthcare Tech Industry

2024 maran qalltawinaka: 5.508 Qalltawinakaw Industria Tecnológica de Salud ukan sutinchata

Стартъпи на 2024 г.: 5 508 стартиращи фирми, номинирани в технологичната индустрия на здравеопазването

Startups del año 2024: 5.508 startups nominadas en la industria de tecnología sanitaria

स्टार्टअप्स ऑफ द ईयर 2024: हेल्थकेयर टेक इंडस्ट्री में 5,508 स्टार्टअप नामांकित

Startup Tahun 2024: 5.508 Startup Masuk Nominasi di Industri Teknologi Kesehatan

2024年のスタートアップ・オブ・ザ・イヤー：ヘルスケア・テック業界で5,508社のスタートアップがノミネート

2024 жылдың стартаптары: денсаулық сақтау саласындағы 5 508 стартап ұсынылды

2024년 올해의 스타트업: 헬스케어 기술 산업에 지명된 5,508개 스타트업

स्टार्टअप अफ द इयर २०२४: ५,५०८ स्टार्टअपहरू हेल्थकेयर टेक उद्योगमा मनोनीत

Стартапи године 2024: 5.508 стартапа номинованих у здравственој технолошкој индустрији

Årets startups 2024: 5 508 startups nominerade inom Healthcare Tech Industry

Mga Startup ng Taon 2024: 5,508 Startup na Nominado sa Healthcare Tech Industry

Authors:
(1) Pinelopi Papalampidi, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh;
(2) Frank Keller, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh;
(3) Mirella Lapata, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh. Table of Links Abstract and Intro
Related Work
Problem Formulation
Experimental Setup
Results and Analysis
Conclusions and References
A. Model Details
B. Implementation Details
C. Results: Ablation Studies Abstract Movie trailers perform multiple functions: they introduce viewers to the story, convey the mood and artistic style of the film, and encourage audiences to see the movie. These diverse functions make automatic trailer generation a challenging endeavor. We decompose it into two subtasks: narrative structure identification and sentiment prediction. We model movies as graphs, where nodes are shots and edges denote semantic relations between them. We learn these relations using joint contrastive training which leverages privileged textual information (e.g., characters, actions, situations) from screenplays. An unsupervised algorithm then traverses the graph and generates trailers that human judges prefer to ones generated by competitive supervised approaches. 1. Introduction Trailers are short videos used for promoting movies and are often critical to commercial success. While their core function is to market the film to a range of audiences, trailers are also a form of persuasive art and promotional narrative, designed to make viewers want to see the movie. Even though the making of trailers is considered an artistic endeavor, the film industry has developed strategies guiding trailer construction. According to one school of thought, trailers must exhibit a narrative structure, consisting of three acts[1]. The first act establishes the characters and setup of the story, the second act introduces the main conflict, and the third act raises the stakes and provides teasers from the ending. Another school of thought is more concerned with the mood of the trailer as defined by the ups and downs of the story[2]. According to this approach, trailers should have medium intensity at first in order to captivate viewers, followed by low intensity for delivering key information about the story, and then progressively increasing intensity until reaching a climax at the end of the trailer. To create trailers automatically, we need to perform lowlevel tasks such as person identification, action recognition, and sentiment prediction, but also more high-level ones such as understanding connections between events and their causality, as well as drawing inferences about the characters and their actions. Given the complexity of the task, directly learning all this knowledge from movie-trailer pairs would require many thousands of examples, whose processing and annotation would be a challenge. It is thus not surprising that previous approaches to automatic trailer generation [24,46,53] have solely focused on audiovisual features. Inspired by the creative process of human editors, we adopt a bottom-up approach to trailer generation, which we decompose into two orthogonal, simpler and well-defined subtasks. The first is the identification of narrative structure, i.e., retrieving the most important events of the movie. A commonly adopted theory in screenwriting [13,22,51] suggests that there are five types of key events in a movie’s plot, known as turning points (TPs; see their definitions in Figure 1). The second subtask is sentiment prediction, which we view as an approximation of flow of intensity between shots and the emotions evoked. We generate proposal trailers following an unsupervised graph-based approach. We model movies as graphs whose nodes are shots and whose edges denote important semantic connections between shots (see Figure 2). In addition, nodes bear labels denoting whether they are key events (i.e., TPs) and scores signaling sentiment intensity (positive or negative). Our algorithm traverses this movie graph to create trailer sequences. These could be used as proposals to be reviewed and modified by a human editor. Both the tasks of TP identification and sentiment prediction stand to benefit from a lower-level understanding of movie content. Indeed, we could employ off-theshelf modules for identifying characters and places, recognizing actions, and localizing semantic units. However, such approaches substantially increase pre-processing time and memory requirements during training and inference and suffer from error propagation. Instead, we propose a contrastive learning regime, where we take advantage of screenplays as privileged information, i.e., information available at training time only. Screenplays reveal how the movie is segmented into scenes, who the characters are, when and who they are speaking to, where they are and what they are doing (i.e., “scene headings” explain where the action takes place while “action lines” describe what the camera sees). Specifically, we build two individual networks, a textual network based on screenplays and a multimodal one based on video, and train them jointly using auxiliary contrastive losses. The textual network can additionally be pretrained on large collections of screenplays via self-supervised learning, without having to collect and process the corresponding movies. Experimental results show that this contrastive training approach is beneficial, leading to trailers which are judged favorably by humans in terms of their content and attractiveness. This paper is available on arxiv under CC BY-SA 4.0 DEED license. [1] https://www.studiobinder.com/blog/how-to-make-a-movie-trailer [2] https://www.derek-lieu.com/blog/2017/9/10/the-matrix-is-a-trailereditors-dream Authors: (1) Pinelopi Papalampidi, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh; (2) Frank Keller, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh; (3) Mirella Lapata, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh. Authors: Authors: (1) Pinelopi Papalampidi, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh; (2) Frank Keller, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh; (3) Mirella Lapata, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh. Table of Links Abstract and Intro Related Work Problem Formulation Experimental Setup Results and Analysis Conclusions and References A. Model Details B. Implementation Details C. Results: Ablation Studies Abstract and Intro Abstract and Intro Related Work Related Work Problem Formulation Problem Formulation Experimental Setup Experimental Setup Results and Analysis Results and Analysis Conclusions and References Conclusions and References A. Model Details A. Model Details B. Implementation Details B. Implementation Details C. Results: Ablation Studies C. Results: Ablation Studies Abstract Movie trailers perform multiple functions: they introduce viewers to the story, convey the mood and artistic style of the film, and encourage audiences to see the movie. These diverse functions make automatic trailer generation a challenging endeavor. We decompose it into two subtasks: narrative structure identification and sentiment prediction. We model movies as graphs, where nodes are shots and edges denote semantic relations between them. We learn these relations using joint contrastive training which leverages privileged textual information (e.g., characters, actions, situations) from screenplays. An unsupervised algorithm then traverses the graph and generates trailers that human judges prefer to ones generated by competitive supervised approaches. 1. Introduction Trailers are short videos used for promoting movies and are often critical to commercial success. While their core function is to market the film to a range of audiences, trailers are also a form of persuasive art and promotional narrative, designed to make viewers want to see the movie. Even though the making of trailers is considered an artistic endeavor, the film industry has developed strategies guiding trailer construction. According to one school of thought, trailers must exhibit a narrative structure, consisting of three acts[1]. The first act establishes the characters and setup of the story, the second act introduces the main conflict, and the third act raises the stakes and provides teasers from the ending. Another school of thought is more concerned with the mood of the trailer as defined by the ups and downs of the story[2]. According to this approach, trailers should have medium intensity at first in order to captivate viewers, followed by low intensity for delivering key information about the story, and then progressively increasing intensity until reaching a climax at the end of the trailer. To create trailers automatically, we need to perform lowlevel tasks such as person identification, action recognition, and sentiment prediction, but also more high-level ones such as understanding connections between events and their causality, as well as drawing inferences about the characters and their actions. Given the complexity of the task, directly learning all this knowledge from movie-trailer pairs would require many thousands of examples, whose processing and annotation would be a challenge. It is thus not surprising that previous approaches to automatic trailer generation [24,46,53] have solely focused on audiovisual features. Inspired by the creative process of human editors, we adopt a bottom-up approach to trailer generation, which we decompose into two orthogonal, simpler and well-defined subtasks. The first is the identification of narrative structure, i.e., retrieving the most important events of the movie. A commonly adopted theory in screenwriting [13,22,51] suggests that there are five types of key events in a movie’s plot, known as turning points (TPs; see their definitions in Figure 1). The second subtask is sentiment prediction, which we view as an approximation of flow of intensity between shots and the emotions evoked. We generate proposal trailers following an unsupervised graph-based approach. We model movies as graphs whose nodes are shots and whose edges denote important semantic connections between shots (see Figure 2). In addition, nodes bear labels denoting whether they are key events (i.e., TPs) and scores signaling sentiment intensity (positive or negative). Our algorithm traverses this movie graph to create trailer sequences. These could be used as proposals to be reviewed and modified by a human editor. Both the tasks of TP identification and sentiment prediction stand to benefit from a lower-level understanding of movie content. Indeed, we could employ off-theshelf modules for identifying characters and places, recognizing actions, and localizing semantic units. However, such approaches substantially increase pre-processing time and memory requirements during training and inference and suffer from error propagation. Instead, we propose a contrastive learning regime, where we take advantage of screenplays as privileged information, i.e., information available at training time only. Screenplays reveal how the movie is segmented into scenes, who the characters are, when and who they are speaking to, where they are and what they are doing (i.e., “scene headings” explain where the action takes place while “action lines” describe what the camera sees). Specifically, we build two individual networks, a textual network based on screenplays and a multimodal one based on video, and train them jointly using auxiliary contrastive losses. The textual network can additionally be pretrained on large collections of screenplays via self-supervised learning, without having to collect and process the corresponding movies. Experimental results show that this contrastive training approach is beneficial, leading to trailers which are judged favorably by humans in terms of their content and attractiveness. This paper is available on arxiv under CC BY-SA 4.0 DEED license. This paper is available on arxiv under CC BY-SA 4.0 DEED license. available on arxiv [1] https://www.studiobinder.com/blog/how-to-make-a-movie-trailer [2] https://www.derek-lieu.com/blog/2017/9/10/the-matrix-is-a-trailereditors-dream

Film Trailer Generation via Task Decomposition: Abstract and Intro

টাস্ক ডিকম্পোজিশনের মাধ্যমে ফিল্ম ট্রেলার জেনারেশন: অ্যাবস্ট্রাক্ট এবং ইন্ট্রো

Filmtrailer-Generierung durch Task Decomposition: Abstract und Intro

Generación de avances de películas mediante descomposición de tareas: resumen e introducción

Génération de bande-annonce de film via la décomposition de tâches : résumé et introduction

टास्क डिकंपोज़िशन के माध्यम से फ़िल्म ट्रेलर तैयार करना: सार और परिचय

タスク分解による映画予告編生成: 概要と導入

작업 분해를 통한 영화 예고편 생성: 초록 및 소개

Geração de trailer de filme por meio de decomposição de tarefas: resumo e introdução

Создание трейлера к фильму посредством декомпозиции задач: аннотация и введение

Görev Ayrıştırma Yoluyla Film Fragmanı Oluşturma: Özet ve Giriş

Tạo đoạn giới thiệu phim thông qua phân tách tác vụ: Tóm tắt và giới thiệu

通过任务分解生成电影预告片：摘要和简介

Authors:
(1) Pinelopi Papalampidi, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh;
(2) Frank Keller, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh;
(3) Mirella Lapata, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh. Table of Links Abstract and Intro
Related Work
Problem Formulation
Experimental Setup
Results and Analysis
Conclusions and References
A. Model Details
B. Implementation Details
C. Results: Ablation Studies 2. Related Work Previous approaches to movie understanding have mainly focused on isolated video clips, and tasks such as the alignment between movie scenes and book chapters [49], question answering [50], video captioning for movie shots [44], and text-to-video retrieval [5]. Recent work [40–42] attempts to identify high-level narrative structure and summarize entire TV episodes and movies focusing exclusively on the textual modality (i.e., screenplays). Existing approaches to trailer generation exploit superficial audiovisual features, such as background music or visual changes between sequential shots [24, 46]. Other work creates “attractive” trailers with a graph-based model for shot selection [57] or uses a human in the loop in conjunction with a model trained on horror movies via audiovisual sentiment analysis [47]. The Trailer Moment Detection Dataset [53] consists of full-length movies paired with official trailers and annotations for key moments, but it is not publicly available and does not include screenplays. Knowledge distillation [3, 23] was originally proposed for distilling information from a larger teacher model to a smaller student one. Generalized distillation [30] provides a framework for using privileged information, i.e., information which is available at training time only. Most related to our work is the use of different modalities or views of the same content [33, 34], e.g., transcribed narrations to learn visual representations in instructional videos. We leverage screenplays as a source of privileged information and distill knowledge about events, characters, and scenes in a film, which we subsequently exploit for identifying trailer worthy shots in video. This paper is available on arxiv under CC BY-SA 4.0 DEED license. Authors: (1) Pinelopi Papalampidi, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh; (2) Frank Keller, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh; (3) Mirella Lapata, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh. Authors: Authors: (1) Pinelopi Papalampidi, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh; (2) Frank Keller, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh; (3) Mirella Lapata, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh. Table of Links Abstract and Intro Related Work Problem Formulation Experimental Setup Results and Analysis Conclusions and References A. Model Details B. Implementation Details C. Results: Ablation Studies Abstract and Intro Abstract and Intro Related Work Related Work Problem Formulation Problem Formulation Experimental Setup Experimental Setup Results and Analysis Results and Analysis Conclusions and References Conclusions and References A. Model Details A. Model Details B. Implementation Details B. Implementation Details C. Results: Ablation Studies C. Results: Ablation Studies 2. Related Work Previous approaches to movie understanding have mainly focused on isolated video clips, and tasks such as the alignment between movie scenes and book chapters [49], question answering [50], video captioning for movie shots [44], and text-to-video retrieval [5]. Recent work [40–42] attempts to identify high-level narrative structure and summarize entire TV episodes and movies focusing exclusively on the textual modality (i.e., screenplays). movie understanding Existing approaches to trailer generation exploit superficial audiovisual features, such as background music or visual changes between sequential shots [24, 46]. Other work creates “attractive” trailers with a graph-based model for shot selection [57] or uses a human in the loop in conjunction with a model trained on horror movies via audiovisual sentiment analysis [47]. The Trailer Moment Detection Dataset [53] consists of full-length movies paired with official trailers and annotations for key moments, but it is not publicly available and does not include screenplays. trailer generation Knowledge distillation [3, 23] was originally proposed for distilling information from a larger teacher model to a smaller student one. Generalized distillation [30] provides a framework for using privileged information, i.e., information which is available at training time only. Most related to our work is the use of different modalities or views of the same content [33, 34], e.g., transcribed narrations to learn visual representations in instructional videos. We leverage screenplays as a source of privileged information and distill knowledge about events, characters, and scenes in a film, which we subsequently exploit for identifying trailer worthy shots in video. Knowledge distillation This paper is available on arxiv under CC BY-SA 4.0 DEED license. This paper is available on arxiv under CC BY-SA 4.0 DEED license. available on arxiv

Film Trailer Generation via Task Decomposition: Related Work

টাস্ক ডিকম্পোজিশনের মাধ্যমে ফিল্ম ট্রেলার জেনারেশন: সম্পর্কিত কাজ

Filmtrailer-Generierung durch Aufgabenzerlegung: Verwandte Arbeiten

Generación de avances de películas mediante descomposición de tareas: trabajo relacionado

Génération de bandes-annonces de films via la décomposition de tâches : travaux connexes

टास्क डिकंपोज़िशन के माध्यम से फ़िल्म ट्रेलर निर्माण: संबंधित कार्य

タスク分解による映画予告編生成: 関連研究

작업 분해를 통한 영화 예고편 생성: 관련 작업

Geração de trailer de filme por meio de decomposição de tarefas: trabalho relacionado

Создание трейлера к фильму посредством декомпозиции задач: соответствующая работа

Görev Ayrıştırma Yoluyla Film Fragmanı Oluşturma: İlgili Çalışma

Tạo đoạn giới thiệu phim thông qua phân tách nhiệm vụ: Công việc liên quan

通过任务分解生成电影预告片：相关工作

Authors:
(1) Pinelopi Papalampidi, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh;
(2) Frank Keller, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh;
(3) Mirella Lapata, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh. Table of Links Abstract and Intro
Related Work
Problem Formulation
Experimental Setup
Results and Analysis
Conclusions and References
A. Model Details
B. Implementation Details
C. Results: Ablation Studies 3. Problem Formulation Trailer generation requires the selection of L shots from a full-length movie of M shots (L  M). Movies present complex stories that may contain distinct subplots or events that unfold non-linearly, while redundant events, called “fillers” enrich the main story. Hence, we cannot assume that consecutive shots are necessarily semantically related. To better explore relations between events, we represent movies as graphs [42]. Let G = (V, E) denote a graph where vertices V are shots and edges E represent their semantic similarity. We further consider the original temporal order of shots in G by only allowing directed edges from previous to future shots. G is described by an upper triangular transition matrix T , which records the probability of transitioning from shot i to every future shot j. Within G, we assume that some shots describe key events in the movie (thick circles in Figure 2) while all shots have a sentiment (positive or negative), whose intensity is denoted by a score (shades of green/red in Figure 2). We propose an algorithm for traversing G and selecting sequences of trailer shots. In the following, we first describe this algorithm (Section 3.1) and then discuss how the graph G is learned and key events are detected via TP identification [41] (Section 3.2). Finally, we also explain how shot-based sentiment scores are predicted (Section 3.5). 3.1. Movie Graph Traversal We select L shots in total (depending on a target trailer length) and retrieve a proposal trailer sequence as depicted in Figure 2 (bold line). At each step, we keep track of the sentiment flow created and the TPs identified thus far (lines 10 and 13–14 in Algorithm 1, respectively). A TP event has been selected for presentation in the trailer if a shot or its immediate neighbors have been added to the path. 3.2. TP Identification The video-based model assumes access to shot-level TP labels. However, the only dataset for TP identification we are aware of is TRIPOD [41], which contains scene-level labels based on screenplays. To obtain more fine-grained labels, we project scene-based annotations to shots following a simple one-to-many mapping (see Section 4 for details). Since our training signal is unavoidably noisy, we hypothesize that access to screenplays would encourage the videobased model to select shots which are more representative for each TP. In other words, screenplays represent privileged knowledge and an implicit supervision signal, while alleviating the need for additional pre-processing during inference. Moreover, screenplays provide a wealth of additional information, e.g., about characters and their roles in a scene, or their actions and emotions (conveyed by lines describing what the camera sees). This information might otherwise be difficult to accurately localize in video. Also, unlabeled text corpora of screenplays are relatively easy to obtain and can be used to pre-train our network. 3.3. Knowledge Distillation We now describe our joint training regime for the two networks which encapsulate different views of the movie in terms of data streams (multimodal vs. text-only) and their segmentation into semantic units (shots vs. scenes). Representation Consistency Loss We propose using a second regularization loss between the two networks in order to also enforce consistency between the two graphbased representations (i.e., over video shots and screenplay scenes). The purpose of this loss is twofold: to improve TP predictions for the two networks, as shown in previous work on contrastive representation learning [38, 39, 48], and also to help learn more accurate connections between shots (recall that the shot-based graph serves as input to our trailer generation algorithm; Section 3.1). In comparison with screenplay scenes, which describe self-contained events in a movie, video shots are only a few seconds long and rely on surrounding context for their meaning. We hypothesize that by enforcing the graph neighborhood for a shot to preserve semantics similar to the corresponding screenplay scene, we will encourage the selection of appropriate neighbors in the shot-based graph. 3.4. Self-supervised Pretraining Pretraining aims to learn better scene representations from screenplays which are more accessible than movie videos (e.g., fewer copyright issues and less computational overhead) in the hope that this knowledge will transfer to the video-based network via our consistency losses. 3.5. Sentiment Prediction Finally, our model takes into account how sentiment flows from one shot to the next. We predict sentiment scores per shot with the same joint architecture (Section 3.3) and training regime we use for TP identification. The video-based network is trained on shots with sentiment labels (i.e., positive, negative, neutral), while the screenplay-based network is trained on scenes with sentiment labels (Section 4 explains how the labels are obtained). After training, we predict a probability distribution over sentiment labels per shot to capture sentiment flow and discriminate between high- and low-intensity shots (see Appendix for details). This paper is available on arxiv under CC BY-SA 4.0 DEED license. Authors: (1) Pinelopi Papalampidi, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh; (2) Frank Keller, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh; (3) Mirella Lapata, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh. Authors: Authors: (1) Pinelopi Papalampidi, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh; (2) Frank Keller, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh; (3) Mirella Lapata, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh. Table of Links Abstract and Intro Related Work Problem Formulation Experimental Setup Results and Analysis Conclusions and References A. Model Details B. Implementation Details C. Results: Ablation Studies Abstract and Intro Abstract and Intro Related Work Related Work Problem Formulation Problem Formulation Experimental Setup Experimental Setup Results and Analysis Results and Analysis Conclusions and References Conclusions and References A. Model Details A. Model Details B. Implementation Details B. Implementation Details C. Results: Ablation Studies C. Results: Ablation Studies 3. Problem Formulation Trailer generation requires the selection of L shots from a full-length movie of M shots (L  M). Movies present complex stories that may contain distinct subplots or events that unfold non-linearly, while redundant events, called “fillers” enrich the main story. Hence, we cannot assume that consecutive shots are necessarily semantically related. To better explore relations between events, we represent movies as graphs [42]. Let G = (V, E) denote a graph where vertices V are shots and edges E represent their semantic similarity. We further consider the original temporal order of shots in G by only allowing directed edges from previous to future shots. G is described by an upper triangular transition matrix T , which records the probability of transitioning from shot i to every future shot j. Within G, we assume that some shots describe key events in the movie (thick circles in Figure 2) while all shots have a sentiment (positive or negative), whose intensity is denoted by a score (shades of green/red in Figure 2). We propose an algorithm for traversing G and selecting sequences of trailer shots. In the following, we first describe this algorithm (Section 3.1) and then discuss how the graph G is learned and key events are detected via TP identification [41] (Section 3.2). Finally, we also explain how shot-based sentiment scores are predicted (Section 3.5). 3.1. Movie Graph Traversal We select L shots in total (depending on a target trailer length) and retrieve a proposal trailer sequence as depicted in Figure 2 (bold line). At each step, we keep track of the sentiment flow created and the TPs identified thus far (lines 10 and 13–14 in Algorithm 1, respectively). A TP event has been selected for presentation in the trailer if a shot or its immediate neighbors have been added to the path. 3.2. TP Identification The video-based model assumes access to shot-level TP labels. However, the only dataset for TP identification we are aware of is TRIPOD [41], which contains scene-level labels based on screenplays. To obtain more fine-grained labels, we project scene-based annotations to shots following a simple one-to-many mapping (see Section 4 for details). Since our training signal is unavoidably noisy, we hypothesize that access to screenplays would encourage the videobased model to select shots which are more representative for each TP. In other words, screenplays represent privileged knowledge and an implicit supervision signal, while alleviating the need for additional pre-processing during inference. Moreover, screenplays provide a wealth of additional information, e.g., about characters and their roles in a scene, or their actions and emotions (conveyed by lines describing what the camera sees). This information might otherwise be difficult to accurately localize in video. Also, unlabeled text corpora of screenplays are relatively easy to obtain and can be used to pre-train our network. 3.3. Knowledge Distillation We now describe our joint training regime for the two networks which encapsulate different views of the movie in terms of data streams (multimodal vs. text-only) and their segmentation into semantic units (shots vs. scenes). Representation Consistency Loss We propose using a second regularization loss between the two networks in order to also enforce consistency between the two graphbased representations (i.e., over video shots and screenplay scenes). The purpose of this loss is twofold: to improve TP predictions for the two networks, as shown in previous work on contrastive representation learning [38, 39, 48], and also to help learn more accurate connections between shots (recall that the shot-based graph serves as input to our trailer generation algorithm; Section 3.1). In comparison with screenplay scenes, which describe self-contained events in a movie, video shots are only a few seconds long and rely on surrounding context for their meaning. We hypothesize that by enforcing the graph neighborhood for a shot to preserve semantics similar to the corresponding screenplay scene, we will encourage the selection of appropriate neighbors in the shot-based graph. Representation Consistency Loss 3.4. Self-supervised Pretraining Pretraining aims to learn better scene representations from screenplays which are more accessible than movie videos (e.g., fewer copyright issues and less computational overhead) in the hope that this knowledge will transfer to the video-based network via our consistency losses. 3.5. Sentiment Prediction Finally, our model takes into account how sentiment flows from one shot to the next. We predict sentiment scores per shot with the same joint architecture (Section 3.3) and training regime we use for TP identification. The video-based network is trained on shots with sentiment labels (i.e., positive, negative, neutral), while the screenplay-based network is trained on scenes with sentiment labels (Section 4 explains how the labels are obtained). After training, we predict a probability distribution over sentiment labels per shot to capture sentiment flow and discriminate between high- and low-intensity shots (see Appendix for details). This paper is available on arxiv under CC BY-SA 4.0 DEED license. This paper is available on arxiv under CC BY-SA 4.0 DEED license. available on arxiv

Film Trailer Generation via Task Decomposition: Problem Formulation

টাস্ক পচনের মাধ্যমে ফিল্ম ট্রেলার জেনারেশন: সমস্যা প্রণয়ন

Filmtrailer-Generierung durch Aufgabenzerlegung: Problemformulierung

Generación de avances de películas mediante descomposición de tareas: formulación de problemas

Génération de bandes-annonces de films via la décomposition de tâches : formulation du problème

कार्य विघटन के माध्यम से फिल्म ट्रेलर निर्माण: समस्या निर्माण

タスク分解による映画予告編生成：問題の定式化

작업 분해를 통한 영화 예고편 생성: 문제 공식화

Geração de trailer de filme por meio de decomposição de tarefas: formulação de problemas

Создание трейлера к фильму посредством декомпозиции задач: формулировка проблемы

Görev Ayrıştırma Yoluyla Film Fragmanı Oluşturma: Problem Formülasyonu

Tạo đoạn giới thiệu phim thông qua phân tách nhiệm vụ: Xây dựng vấn đề

通过任务分解生成电影预告片：问题公式化

Film Trailer Generation via Task Decomposition: Conclusions and References

টাস্ক ডিকম্পোজিশনের মাধ্যমে ফিল্ম ট্রেলার জেনারেশন: উপসংহার এবং রেফারেন্স

Filmtrailer-Generierung durch Aufgabenzerlegung: Schlussfolgerungen und Referenzen

Generación de avances de películas mediante descomposición de tareas: conclusiones y referencias

Génération de bandes-annonces de films via la décomposition de tâches : conclusions et références

टास्क डिकंपोज़िशन के माध्यम से फ़िल्म ट्रेलर निर्माण: निष्कर्ष और संदर्भ

タスク分解による映画予告編生成: 結論と参考文献

작업 분해를 통한 영화 예고편 생성: 결론 및 참고 자료

Geração de trailer de filme por meio de decomposição de tarefas: conclusões e referências

Генерация трейлеров к фильму посредством декомпозиции задач: выводы и ссылки

Görev Ayrıştırma Yoluyla Film Fragmanı Oluşturma: Sonuçlar ve Referanslar

Tạo đoạn giới thiệu phim thông qua phân tách nhiệm vụ: Kết luận và tài liệu tham khảo

通过任务分解生成电影预告片：结论与参考文献

Film Trailer Generation via Task Decomposition: Results and Analysis

টাস্ক পচনের মাধ্যমে ফিল্ম ট্রেলার জেনারেশন: ফলাফল এবং বিশ্লেষণ

Filmtrailer-Generierung durch Aufgabenzerlegung: Ergebnisse und Analyse

Generación de avances de películas mediante descomposición de tareas: resultados y análisis

Génération de bandes-annonces de films via la décomposition des tâches : résultats et analyse

टास्क डिकंपोज़िशन के माध्यम से फ़िल्म ट्रेलर निर्माण: परिणाम और विश्लेषण

タスク分解による映画予告編生成: 結果と分析

작업 분해를 통한 영화 예고편 생성: 결과 및 분석

Geração de trailer de filme por meio de decomposição de tarefas: resultados e análise

Генерация трейлера к фильму посредством декомпозиции задач: результаты и анализ

Görev Ayrıştırma Yoluyla Film Fragmanı Oluşturma: Sonuçlar ve Analiz

Tạo đoạn giới thiệu phim thông qua phân tách nhiệm vụ: Kết quả và phân tích

通过任务分解生成电影预告片：结果与分析

Authors:
(1) Pinelopi Papalampidi, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh;
(2) Frank Keller, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh;
(3) Mirella Lapata, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh. Table of Links Abstract and Intro
Related Work
Problem Formulation
Experimental Setup
Results and Analysis
Conclusions and References
A. Model Details
B. Implementation Details
C. Results: Ablation Studies B. Implementation Details Evaluation Metrics Previous work [41] evaluates the performance of TP identification models in terms of three metrics: Total Agreement (TA), i.e., the percentage of TP scenes that are correctly identified, Partial Agreement (PA), i.e., the percentage of TP events for which at least one gold-standard scene is identified, and Distance (D), i.e., the minimum distance in number of scenes between the predicted and gold-standard set of scenes for a given TP, normalized by the screenplay length. We report results with the partial agreement metric. We can no longer use total agreement, since we evaluate against silver standard (rather than gold) labels for shots (rather than scenes) and as a result consider all shots within a scene equally important. We do not use the distance metric either since it yields very similar outcomes and does not help discriminate among model variants. Hyperparameters Following previous work [42], we project all types of features (i.e., textual, visual, and audio) to the same lower dimension of 128. We find that larger dimensions increase the number of parameters considerably and yield inferior results possibly due to small dataset size. We contextualize scenes (with respect to the screenplay) and shots (with respect to the video) using transformer encoders. We experimented with 2, 3, 4, 5, and 6 layers in the encoder and obtained best results with 3 layers. For the feed forward (FF) dimension, we experimented with both a standard size of 2,048 and a smaller size of 1,024 and found the former works better. We use another transformer encoder to compute the representation of a scene from a sequence of input sentence representations. This encoder has 4 layers and 1,024 FF dimension. Both encoders, employ 8 attention heads and 0.3 dropout. During graph sparsification (i.e., selection of top-k neighbors), we consider different neighborhood options for the scene- and shot-based networks due to their different granularity and size. Following [42], we consider [1–6] neighbors for the scene network and we increase the neighborhood size to [6–12] for the shot network. This paper is available on arxiv under CC BY-SA 4.0 DEED license. Authors: (1) Pinelopi Papalampidi, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh; (2) Frank Keller, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh; (3) Mirella Lapata, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh. Authors: Authors: (1) Pinelopi Papalampidi, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh; (2) Frank Keller, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh; (3) Mirella Lapata, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh. Table of Links Abstract and Intro Related Work Problem Formulation Experimental Setup Results and Analysis Conclusions and References A. Model Details B. Implementation Details C. Results: Ablation Studies Abstract and Intro Abstract and Intro Related Work Related Work Problem Formulation Problem Formulation Experimental Setup Experimental Setup Results and Analysis Results and Analysis Conclusions and References Conclusions and References A. Model Details A. Model Details B. Implementation Details B. Implementation Details C. Results: Ablation Studies C. Results: Ablation Studies B. Implementation Details Evaluation Metrics Previous work [41] evaluates the performance of TP identification models in terms of three metrics: Total Agreement (TA), i.e., the percentage of TP scenes that are correctly identified, Partial Agreement (PA), i.e., the percentage of TP events for which at least one gold-standard scene is identified, and Distance (D), i.e., the minimum distance in number of scenes between the predicted and gold-standard set of scenes for a given TP, normalized by the screenplay length. We report results with the partial agreement metric. We can no longer use total agreement, since we evaluate against silver standard (rather than gold) labels for shots (rather than scenes) and as a result consider all shots within a scene equally important. We do not use the distance metric either since it yields very similar outcomes and does not help discriminate among model variants. Evaluation Metrics Hyperparameters Following previous work [42], we project all types of features (i.e., textual, visual, and audio) to the same lower dimension of 128. We find that larger dimensions increase the number of parameters considerably and yield inferior results possibly due to small dataset size. Hyperparameters We contextualize scenes (with respect to the screenplay) and shots (with respect to the video) using transformer encoders. We experimented with 2, 3, 4, 5, and 6 layers in the encoder and obtained best results with 3 layers. For the feed forward (FF) dimension, we experimented with both a standard size of 2,048 and a smaller size of 1,024 and found the former works better. We use another transformer encoder to compute the representation of a scene from a sequence of input sentence representations. This encoder has 4 layers and 1,024 FF dimension. Both encoders, employ 8 attention heads and 0.3 dropout. During graph sparsification (i.e., selection of top-k neighbors), we consider different neighborhood options for the scene- and shot-based networks due to their different granularity and size. Following [42], we consider [1–6] neighbors for the scene network and we increase the neighborhood size to [6–12] for the shot network. This paper is available on arxiv under CC BY-SA 4.0 DEED license. This paper is available on arxiv under CC BY-SA 4.0 DEED license. available on arxiv

Film Trailer Generation via Task Decomposition: Implementation Details

টাস্ক ডিকম্পোজিশনের মাধ্যমে ফিল্ম ট্রেলার জেনারেশন: বাস্তবায়নের বিবরণ

Filmtrailer-Generierung mittels Task Decomposition: Implementierungsdetails

Generación de avances de películas mediante descomposición de tareas: detalles de implementación

Génération de bandes-annonces de film via la décomposition des tâches : détails de mise en œuvre

टास्क डिकंपोज़िशन के माध्यम से फ़िल्म ट्रेलर निर्माण: कार्यान्वयन विवरण

タスク分解による映画予告編生成: 実装の詳細

작업 분해를 통한 영화 예고편 생성: 구현 세부 사항

Geração de trailer de filme por meio de decomposição de tarefas: detalhes de implementação

Генерация трейлера к фильму посредством декомпозиции задач: детали реализации

Görev Ayrıştırma Yoluyla Film Fragmanı Oluşturma: Uygulama Ayrıntıları

Tạo đoạn giới thiệu phim thông qua phân tách nhiệm vụ: Chi tiết triển khai

通过任务分解生成电影预告片：实现细节

Authors:
(1) Pinelopi Papalampidi, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh;
(2) Frank Keller, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh;
(3) Mirella Lapata, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh. Table of Links Abstract and Intro
Related Work
Problem Formulation
Experimental Setup
Results and Analysis
Conclusions and References
A. Model Details
B. Implementation Details
C. Results: Ablation Studies A. Model Details In this section we provide details about the various modeling components of our approach. We begin by providing details of the GRAPHTRAILER architecture (Section A.1), then move to discuss how the TP identification network is trained (Section A.2), and finally give technical details about pre-training on screenplays (A.3), and the sentiment flow used for graph traversal (A.4). A.1. GRAPHTRAILER We address discontinuities in our model (i.e., top-k sampling, neighborhood size selection) by utilising the StraightThrough Estimator [7]. During the backward pass we compute the gradients with the Gumbel-softmax reparametrization trick [25, 32]. The same procedure is followed for constructing and sparsifying scene-level graphs in the auxiliary screenplay-based network. A.2. Training on TP Identification Section 3 presents our training regime for the video- and screenplay-based model assuming TP labels for scenes are available (i.e., binary labels indicating whether a scene acts as a TP in a movie). Given such labels, our model is trained with a binary cross-entropy loss (BCE) objective between the few-hot gold labels and the network’s TP predictions. However, in practice, our training set contains silverstandard labels for scenes. The latter are released together with the TRIPOD [41] dataset and were created automatically. Specifically, TRIPOD provides gold-standard TP annotations for synopses (not screenplays), under the assumption that synopsis sentences are representative of TPs. And sentence-level annotations are projected to scenes with a matching model trained with teacher forcing [41] to create silver-standard labels. A.3. Self-supervised Pre-training A.4. Sentiment Flow in GRAPHTRAILER One of the criteria for selecting the next shot in our graph traversal algorithm (Section 3.1) is the sentiment flow of the trailer generated so far. Specifically, we adopt the hypothesis[9] that trailers are segmented into three sections based on sentiment intensity. The first section has medium intensity for attracting viewers, the second section has low intensity for delivering key information about the movie and finally the third section displays progressively higher intensity for creating cliffhangers and excitement for the movie. Accordingly, given a budget of L trailer shots, we expect the first L/3 ones to have medium intensity without large variations within the section (e.g., we want shots with average absolute intensity close to 0.7, where all scores are normalized to a range from -1 to 1). In the second part of the trailer (i.e., the next L/3 shots) we expect a sharp drop in intensity and shots within this section to maintain more or less neutral sentiment (i.e., 0 intensity). Finally, for the third section (i.e., the final L/3 shots) we expect intensity to steadily increase. In practice, we expect the intensity of the first shot to be 0.7 (i.e., medium intensity), increasing by 0.1 with each subsequent shot until we reach a peak at the final shot. This paper is available on arxiv under CC BY-SA 4.0 DEED license. [9] https : / / www . derek - lieu . com / blog / 2017 / 9 / 10 / the - matrix - is - a - trailer - editors-dream Authors: (1) Pinelopi Papalampidi, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh; (2) Frank Keller, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh; (3) Mirella Lapata, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh. Authors: Authors: (1) Pinelopi Papalampidi, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh; (2) Frank Keller, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh; (3) Mirella Lapata, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh. Table of Links Abstract and Intro Related Work Problem Formulation Experimental Setup Results and Analysis Conclusions and References A. Model Details B. Implementation Details C. Results: Ablation Studies Abstract and Intro Abstract and Intro Related Work Related Work Problem Formulation Problem Formulation Experimental Setup Experimental Setup Results and Analysis Results and Analysis Conclusions and References Conclusions and References A. Model Details A. Model Details B. Implementation Details B. Implementation Details C. Results: Ablation Studies C. Results: Ablation Studies A. Model Details In this section we provide details about the various modeling components of our approach. We begin by providing details of the GRAPHTRAILER architecture (Section A.1), then move to discuss how the TP identification network is trained (Section A.2), and finally give technical details about pre-training on screenplays (A.3), and the sentiment flow used for graph traversal (A.4). A.1. GRAPHTRAILER We address discontinuities in our model (i.e., top-k sampling, neighborhood size selection) by utilising the StraightThrough Estimator [7]. During the backward pass we compute the gradients with the Gumbel-softmax reparametrization trick [25, 32]. The same procedure is followed for constructing and sparsifying scene-level graphs in the auxiliary screenplay-based network. A.2. Training on TP Identification Section 3 presents our training regime for the video- and screenplay-based model assuming TP labels for scenes are available (i.e., binary labels indicating whether a scene acts as a TP in a movie). Given such labels, our model is trained with a binary cross-entropy loss (BCE) objective between the few-hot gold labels and the network’s TP predictions. However, in practice, our training set contains silverstandard labels for scenes. The latter are released together with the TRIPOD [41] dataset and were created automatically. Specifically, TRIPOD provides gold-standard TP annotations for synopses (not screenplays), under the assumption that synopsis sentences are representative of TPs. And sentence-level annotations are projected to scenes with a matching model trained with teacher forcing [41] to create silver-standard labels. A.3. Self-supervised Pre-training A.4. Sentiment Flow in GRAPHTRAILER One of the criteria for selecting the next shot in our graph traversal algorithm (Section 3.1) is the sentiment flow of the trailer generated so far. Specifically, we adopt the hypothesis[9] that trailers are segmented into three sections based on sentiment intensity. The first section has medium intensity for attracting viewers, the second section has low intensity for delivering key information about the movie and finally the third section displays progressively higher intensity for creating cliffhangers and excitement for the movie. Accordingly, given a budget of L trailer shots, we expect the first L/3 ones to have medium intensity without large variations within the section (e.g., we want shots with average absolute intensity close to 0.7, where all scores are normalized to a range from -1 to 1). In the second part of the trailer (i.e., the next L/3 shots) we expect a sharp drop in intensity and shots within this section to maintain more or less neutral sentiment (i.e., 0 intensity). Finally, for the third section (i.e., the final L/3 shots) we expect intensity to steadily increase. In practice, we expect the intensity of the first shot to be 0.7 (i.e., medium intensity), increasing by 0.1 with each subsequent shot until we reach a peak at the final shot. This paper is available on arxiv under CC BY-SA 4.0 DEED license. This paper is available on arxiv under CC BY-SA 4.0 DEED license. available on arxiv [9] https : / / www . derek - lieu . com / blog / 2017 / 9 / 10 / the - matrix - is - a - trailer - editors-dream

Film Trailer Generation via Task Decomposition: Model Details

টাস্ক ডিকম্পোজিশনের মাধ্যমে ফিল্ম ট্রেলার জেনারেশন: মডেলের বিবরণ

Filmtrailer-Generierung durch Aufgabenzerlegung: Modelldetails

Generación de avances de películas mediante descomposición de tareas: detalles del modelo

Génération de bandes-annonces de film via la décomposition de tâches : détails du modèle

टास्क डिकंपोज़िशन के माध्यम से फ़िल्म ट्रेलर निर्माण: मॉडल विवरण

タスク分解による映画予告編生成: モデルの詳細

작업 분해를 통한 영화 예고편 생성: 모델 세부정보

Geração de trailer de filme por meio de decomposição de tarefas: detalhes do modelo

Создание трейлера к фильму посредством декомпозиции задач: детали модели

Görev Ayrıştırma Yoluyla Film Fragmanı Oluşturma: Model Ayrıntıları

Tạo đoạn giới thiệu phim thông qua phân tách nhiệm vụ: Chi tiết mô hình

通过任务分解生成电影预告片：模型细节

Authors:
(1) Pinelopi Papalampidi, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh;
(2) Frank Keller, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh;
(3) Mirella Lapata, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh. Table of Links Abstract and Intro
Related Work
Problem Formulation
Experimental Setup
Results and Analysis
Conclusions and References
A. Model Details
B. Implementation Details
C. Results: Ablation Studies C. Results: Ablation Studies D. Task Decomposition Analysis How Narrative Structure Connects with Trailers According to screenwriting theory [22], the five TPs segment movies into six thematic units, namely, ”Setup”, ”New Situation”, ”Progress”, ”Complications and Higher Stakes”, ”Final Push”, and ”Aftermath”. To examine which parts of the movie are most prevalent in a trailer, we compute the distribution of shots per thematic unit in gold trailers (using the extended development set of TRIPOD). As shown in Figure 4, trailers on average contain shots from all sections of a movie, even from the last two, which might reveal the ending. Moreover, most trailer shots (30.33%) are selected from the middle of the movie (i.e., Progress) as well as from the beginning (i.e., 16.62% and 25.45% for “Setup” and “New Situation”, respectively). These empirical observations corroborate industry principles for trailer creation.[10] Next, we find how often the trailers include the different types of key events denoted by TPs. We present the percentage of trailers (on the development set) that include at least one shot per TP in Table 7. As can be seen, more than half of the trailers (i.e., 52.63% and 55.26%) include shots related to the first two TPs, whereas only 34.21% of trailers have any information about the two final ones. This is expected, since the first TPs are introductory to the story and hence more important for making trailers, whereas the last two may contain spoilers and are often avoided. How Sentiment Connects with Trailers Empirical rules for trailer making[11] suggest that a trailer should start with shots of medium intensity to captivate the viewers, then decrease the sentiment intensity in order to deliver key information about the movie, and finally build up the tension until it reaches a climax. Here, we analyze the sentiment flow in real trailers from our development set based on predicted sentiment scores (see Sections 3.5 and 4). Specifically, we compute the absolute sentiment intensity (i.e., regardless of positive/negative polarity) per shot in the (true) trailers. In accordance with our experimental setup, we again map trailer shots to movie shots based on visual similarity and consider the corresponding sentiment scores predicted by our network. We then segment the trailer into three equal sections and compute the average absolute sentiment intensity per section. In Table 8 presents the results. As expected, on average, the second part is the least intense, whereas the third has the highest sentiment intensity. Finally, when we again segment each trailer into three equal sections and measure the sentiment flow from one section to the next, we find that 46.67% of the trailers follow a ”V” shape, similar to our sentiment condition for generating proposal trailers with GRAPHTRAILER. Examples of Walks in GRAPHTRAILER We present in Figures 5 and 6 a real example of how GRAPHTRAILER operates over a sparse (shot) graph for the movie ”The Shining”. Here, we show the algorithm’s inner workings on a further pruned graph for better visualization (Step 1; Figure 5), while in reality we use the full graph as input to GRAPHTRAILER. We begin with shots that have been identified as TP1 (i.e., ”Opportunity”; introductory event for the story). We sample a shot (i.e., bright green nodes in graph) and initialize our path. For the next steps (2–7; in reality, we execute up to 10 steps, but we excluded a few for brevity), we only examine the immediate neighborhood of the current node and select the next shot to be included in the path based on the following criteria: (1) semantic coherence, (2) time proximity, (3) narrative structure, and (4) sentiment intensity. We give more details about how we formalize and combine these criteria in Section 3.1. We observe that our algorithm manages to stay close to important events (colored nodes) while creating the path, which means that we reduce the probability of selecting random shots that are irrelevant to the main story. Finally, in Step 8, Figure 6, we assemble the proposal trailer by concatenating all shots in the retrieved path. We also illustrate the path in the graph (i.e., red line). An advantage of our approach is that it is interpretable and can be easily used as a tool with a human in the loop. Specifically, given the immediate neighborhood at each step, one could select shots based on different automatic criteria or even manually. Our approach drastically reduces the amount of shots that need to be reviewed to create trailer sequences to 10% of the movie. Moreover, our criteria allow users to explore different sections of the movie, and create diverse trailers. This paper is available on arxiv under CC BY-SA 4.0 DEED license. [10] https://archive.nytimes.com/www.nytimes.com/interactive/2013/02/19/movies/ awardsseason/oscar-trailers.html?_r=0 [11] https://www.derek-lieu.com/blog/2017/9/10/the matrix-is-a-trailer-editors-dream Authors: (1) Pinelopi Papalampidi, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh; (2) Frank Keller, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh; (3) Mirella Lapata, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh. Authors: Authors: (1) Pinelopi Papalampidi, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh; (2) Frank Keller, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh; (3) Mirella Lapata, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburgh. Table of Links Abstract and Intro Related Work Problem Formulation Experimental Setup Results and Analysis Conclusions and References A. Model Details B. Implementation Details C. Results: Ablation Studies Abstract and Intro Abstract and Intro Related Work Related Work Problem Formulation Problem Formulation Experimental Setup Experimental Setup Results and Analysis Results and Analysis Conclusions and References Conclusions and References A. Model Details A. Model Details B. Implementation Details B. Implementation Details C. Results: Ablation Studies C. Results: Ablation Studies C. Results: Ablation Studies D. Task Decomposition Analysis How Narrative Structure Connects with Trailers According to screenwriting theory [22], the five TPs segment movies into six thematic units, namely, ”Setup”, ”New Situation”, ”Progress”, ”Complications and Higher Stakes”, ”Final Push”, and ”Aftermath”. To examine which parts of the movie are most prevalent in a trailer, we compute the distribution of shots per thematic unit in gold trailers (using the extended development set of TRIPOD). As shown in Figure 4, trailers on average contain shots from all sections of a movie, even from the last two, which might reveal the ending. Moreover, most trailer shots (30.33%) are selected from the middle of the movie (i.e., Progress) as well as from the beginning (i.e., 16.62% and 25.45% for “Setup” and “New Situation”, respectively). These empirical observations corroborate industry principles for trailer creation.[10] How Narrative Structure Connects with Trailers Next, we find how often the trailers include the different types of key events denoted by TPs. We present the percentage of trailers (on the development set) that include at least one shot per TP in Table 7. As can be seen, more than half of the trailers (i.e., 52.63% and 55.26%) include shots related to the first two TPs, whereas only 34.21% of trailers have any information about the two final ones. This is expected, since the first TPs are introductory to the story and hence more important for making trailers, whereas the last two may contain spoilers and are often avoided. How Sentiment Connects with Trailers Empirical rules for trailer making[11] suggest that a trailer should start with shots of medium intensity to captivate the viewers, then decrease the sentiment intensity in order to deliver key information about the movie, and finally build up the tension until it reaches a climax. How Sentiment Connects with Trailers Here, we analyze the sentiment flow in real trailers from our development set based on predicted sentiment scores (see Sections 3.5 and 4). Specifically, we compute the absolute sentiment intensity (i.e., regardless of positive/negative polarity) per shot in the (true) trailers. In accordance with our experimental setup, we again map trailer shots to movie shots based on visual similarity and consider the corresponding sentiment scores predicted by our network. We then segment the trailer into three equal sections and compute the average absolute sentiment intensity per section. In Table 8 presents the results. As expected, on average, the second part is the least intense, whereas the third has the highest sentiment intensity. Finally, when we again segment each trailer into three equal sections and measure the sentiment flow from one section to the next, we find that 46.67% of the trailers follow a ”V” shape, similar to our sentiment condition for generating proposal trailers with GRAPHTRAILER. Examples of Walks in GRAPHTRAILER We present in Figures 5 and 6 a real example of how GRAPHTRAILER operates over a sparse (shot) graph for the movie ”The Shining”. Here, we show the algorithm’s inner workings on a further pruned graph for better visualization (Step 1; Figure 5), while in reality we use the full graph as input to GRAPHTRAILER. We begin with shots that have been identified as TP1 (i.e., ”Opportunity”; introductory event for the story). We sample a shot (i.e., bright green nodes in graph) and initialize our path. For the next steps (2–7; in reality, we execute up to 10 steps, but we excluded a few for brevity), we only examine the immediate neighborhood of the current node and select the next shot to be included in the path based on the following criteria: (1) semantic coherence, (2) time proximity, (3) narrative structure, and (4) sentiment intensity. We give more details about how we formalize and combine these criteria in Section 3.1. We observe that our algorithm manages to stay close to important events (colored nodes) while creating the path, which means that we reduce the probability of selecting random shots that are irrelevant to the main story. Finally, in Step 8, Figure 6, we assemble the proposal trailer by concatenating all shots in the retrieved path. We also illustrate the path in the graph (i.e., red line). An advantage of our approach is that it is interpretable and can be easily used as a tool with a human in the loop. Specifically, given the immediate neighborhood at each step, one could select shots based on different automatic criteria or even manually. Our approach drastically reduces the amount of shots that need to be reviewed to create trailer sequences to 10% of the movie. Moreover, our criteria allow users to explore different sections of the movie, and create diverse trailers. This paper is available on arxiv under CC BY-SA 4.0 DEED license. This paper is available on arxiv under CC BY-SA 4.0 DEED license. available on arxiv [10] https://archive.nytimes.com/www.nytimes.com/interactive/2013/02/19/movies/ awardsseason/oscar-trailers.html?_r=0 [11] https://www.derek-lieu.com/blog/2017/9/10/the matrix-is-a-trailer-editors-dream

Film Trailer Generation via Task Decomposition: Results: Ablation Studies

টাস্ক ডিকম্পোজিশনের মাধ্যমে ফিল্ম ট্রেলার জেনারেশন: ফলাফল: অ্যাবলেশন স্টাডিজ

Filmtrailer-Generierung durch Aufgabenzerlegung: Ergebnisse: Ablationsstudien

Generación de avances de películas mediante descomposición de tareas: resultados: estudios de ablación

Génération de bandes-annonces de films via la décomposition de tâches : résultats : études sur l'ablation

टास्क डिकंपोज़िशन के माध्यम से फ़िल्म ट्रेलर निर्माण: परिणाम: एब्लेशन अध्ययन

タスク分解による映画予告編生成: 結果: アブレーション研究

작업 분해를 통한 영화 예고편 생성: 결과: 절제 연구

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