Climax

climax

THE ENTERTAINMENT OF MR. DIRECK REACHES A CLIMAX

For the Story Teller: Story Telling and Stories to Tell, by Carolyn Sherwin Bailey is part of the HackerNoon Books series. You can jump to any chapter in this book here . Chapter V - STORY CLIMAX CHAPTER V. STORY CLIMAX We have found it helpful to liken the effect that a well-written, well-told story has upon a child’s mind to the appeal that a successful drama makes to an audience. We have discovered that the opening paragraph, the first sentence of a child’s story should have the quality that characterizes the scene disclosed on the stage when the curtain rolls up—compelling interest. Following this curtain raising of the story, there should be a series of pictorial scenes that carry the events that go to make up the story plot, strung upon a slender thread of curiosity, and giving the element of suspense to the story. Following out this story structure we come, eventually, to the end. The curtain must fall at last before the eyes of the child audience and the closing of the story drama should be as mind stimulating as was its beginning. This is brought about by studying carefully the story climax. The climax of a story should be a complete surprise to the listener and to the characters in the story, as well. This quick note of the unexpected coming with compelling suddenness at the end of our story clinches the interest of the plot and makes the story indelible on the child’s mind sheet. Certain well-known instances of climax as exemplified in child stories will clarify for us its surprise quality. In one of the older plantation folk tales, Mr. Elephant and Mr. Frog are pictured as being good friends until Mr. Hare taunts them with their dissimilarity in size and says that Mr. Frog has boasted of the fact that Mr. Elephant is his “riding horse.” Then the story continues: “Mr. Fox and Mr. Tiger and Mr. Lion all followed after Mr. Hare, crying: ‘Oho, oho, Mr. Elephant is little Mr. Frog’s riding horse.’ “Then Mr. Elephant turned around and he said in a very gruff voice to Mr. Frog: “‘Did you tell them, grandson, that I was your horse?’ “And Mr. Frog said in a high, squeaky voice:— “‘No, no, grandfather.’ “But all the time Mr. Frog was thinking of a trick to play on Mr. Elephant. “The next day, Mr. Elephant and Mr. Frog started off for a long walk. Mr. Frog had heard of a place where the swamps were deep and muddy. Mr. Elephant knew a place where the bananas grew ripe and thick. And they spent a pleasant day. On the way home Mr. Frog hopped up close to Mr. Elephant, and he said in his high, squeaky voice:— “‘Grandfather, I have no strength to walk. Let me get up on your back.’ “‘Climb up, my grandson,’ said Mr. Elephant. “He put his trunk down for a ladder, and Mr. Frog climbed up. They had not gone very far when Mr. Frog hopped up close to Mr. Elephant’s ear, and he said:— “‘I am going to fall, grandfather. Give me some small cords from the roadside that I may bind your mouth, and hold myself upon your back.’ “‘I will, grandson,’ said Mr. Elephant. “So Mr. Elephant stripped some small cords from a birch tree by the roadside, and handed them to Mr. Frog. Then Mr. Frog bound Mr. Elephant’s mouth, and they went on a little farther. It was not long, though, before Mr. Frog spoke again to Mr. Elephant. “‘Grandfather,’ he said, ‘find me a small green twig that I may fan the mosquitoes from your ears.’ “‘I will, grandson,’ said Mr. Elephant, so he broke a small, green twig from the birch tree, and reached it up to Mr. Frog; and just then they came toward home. “‘See Mr. Elephant,’ cried Mr. Hare. “‘See Mr. Elephant,’ cried Mr. Tiger. “‘See Mr. Elephant,’ cried Mr. Lion, and all the others, ‘Mr. Elephant is Mr. Frog’s horse.’ “Mr. Elephant turned himself about, and he saw Mr. Frog on his back, holding the reins and the whip. “‘Why, so I am, grandson,’ said Mr. Elephant. “Then Mr. Frog jumped down to the ground, and he laughed and he laughed until he nearly split his coat, because he had played a trick on Mr. Elephant.” This quotation serves very well to illustrate perfect story climax. In the beginning of the story, an apparently impossible situation was suggested. To the child listener it seems incredible that an elephant could so far forget his dignity as to serve as the steed of a frog. To the elephant himself, as well, this situation appears to be incompatible with his social status in the jungle. As the story advances, each scene prepares a way for the unexpected dénouement and the climax is found in the surprise to the hearers and to Mr. Elephant as well when the curtain falls upon him unwittingly playing the part of “riding horse” to little Mr. Frog. Hans Christian Andersen’s inimitable allegory of “The Ugly Duckling” owes a measure, at least, of its popularity to its perfect climax. In the beautiful word pictures of the story we follow its hero, the Ugly Duckling, through his series of perilous and sorrowful adventures, sympathizing with but not anticipating the outcome of them. In no single one of the scenes of the story do we have a hint of the glorious ending of the hero’s journeying. Finally comes a quick, artistic curtain falling: —“Then he flew toward the beautiful swans. As soon as they saw him they rushed to meet him with outstretched wings. “‘Kill me!’ said the Ugly Duckling; but as he bent his head, what did he see reflected in the water? It was his own image—not a dark, gray bird, ugly to see—but a graceful swan. “Then the great swans swam around him and stroked his neck with their beaks for a welcome. Some little children came into the garden. “‘See,’ they cried, clapping their hands. “‘A new swan has come and he is more beautiful than the others!’”— This story climax is perfect, also, because it carries the element of surprise to the story hearers and the story hero, the Ugly Duckling, as well. It seems to be almost impossible to find many instances of well constructed climax in the short story for children. The story teller must look for climax and in the event of not being able to find it in the story that she selects for telling, it will be necessary for her to make over her story ending that it may be a complete surprise to her listeners, in this way strengthening the plot greatly. Many stories just stop, giving one a feeling of dissatisfaction. There has been no climax to make of the whole a finished picture, complete in its minutest detail of light and shade and forming an unerasable vignette, on the child’s mind. Climax knots the thread of the narrative. Certain child stories, however, stand out as illuminating instances of what climax means in deepening the mental appeal for the child, etching the story picture, so to speak. Nathaniel Hawthorne’s “Great Stone Face,” forms one instance. A careful reading of the story will disclose Hawthorne’s subtle use of suspense, the art of “making his audience wait” for his dénouement. He makes us see the fertile valley beneath the great mountain upon whose side there had been sculptured by Nature, the wonderful stone face. We are carried, breathlessly, along upon the tide of the narrative through the boy Ernest’s longing to bear the image of these beautiful features, the futile attempts of old Gathergold, old Blood-and-Thunder and the others to prove their likeness to the Great Stone Face until we reach our climax in Ernest’s own transformation into the great likeness unsuspected by himself or by us. It seems to be the great short story writers, only, who have given us really illuminating instances of climax—surprise ending—in stories that will appeal to children in a stimulating way. In Hawthorne’s “Snow Image” the curtain falls upon a surprise situation. Oscar Wilde leaves us unconsolable at his apparent ending of “The Happy Prince.” The little swallow is dead and the Prince has given away his gold and jewels. “So they pulled down the statue of the Happy Prince who was no longer beautiful and so no longer useful and they melted the statue in the furnace. “‘What a strange thing!’ said the overseer of the workmen at the foundry. ‘This broken lead heart will not melt in the furnace. We must throw it away!’ “So they threw it on a dust-heap where the dead swallow was also lying.” But as we hold our breath, the climax is flung gloriously out. “‘Bring me the two most precious things in the City,’ said God to one of His Angels and the Angel brought him the leaden heart and the dead bird. “‘You have rightly chosen,’ said God, ‘for in my garden of Paradise this little bird shall sing for evermore, and in my city of gold the Happy Prince shall praise me.’” In the story of Cosette and her doll in “Les Misérables,” Victor Hugo has given us a complete story vignette with a perfectly developed climax of surprise as Jean Valjean gives to Cosette, the “toad” of Madame Thenardier’s Kitchen, the doll about which she had dreamed. Laura Richard’s short stories for children abound in instances of illuminating climax—no hint of the story ending being given until the curtain falls. Her story of “The Golden Windows” in which a little boy sets out upon a journey to find the windows of gold that he sees beyond the village from his own poor little home and discovers at the end of the day that his own home windows, viewed from a distance are gold and those he has found are gray and dull, is an example of Mrs. Richards’ skilled use of climax to force her story point into the child’s mind. The mental appeal of climax is a very real and vital one for the consideration of the story teller. Once we fix in our minds the two characteristics of artistic story ending, surprise to the story characters and to the children, and an ending that ties the knot of the narrative, our story curtain will fall, leaving the children a lap farther along in their mental development than they were when the curtain rose. 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. Bailey, Carolyn Sherwin. 2018. For the Story Teller: Story Telling and Stories to Tell. Urbana, Illinois: Project Gutenberg. Retrieved April 2022 from https://www.gutenberg.org/files/58107/58107-h/58107-h.htm#Page_83 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 .

For the Story Teller: Chapter 5 - Story Climax

Deadpool and Wolverine is currently taking the world by storm. It has officially surpassed the $1 billion mark and has dethroned Joker to become the highest-grossing rated R film of all time. Everyone’s talking about it, and the one thing that people are talking about the most is all the cameos. MCU movies, especially now that they’re in their Multiverse phase, are filled with a litany of cameos and easter eggs, and Deadpool and Wolverine is no exception. With so many appearing throughout the film, even the most eagle-eyed MCU fan would find it hard to spot them all out. So, to help you out, here are all the Deadpool and Wolverine Cameos in order. Want to share your own thoughts on popular media? Start publishing on HackerNoon today! All the Deadpool and Wolverine Cameos Happy Hogan One of the O.G.s of the MCU, Happy Hogan has been a part of the universe since the very first Iron Man. His character has evolved throughout the years, and in Deadpool and Wolverine, he is seen managing the Avengers. Thor The God of Thunder makes a brief appearance where he’s seen comforting Deadpool on one of the TVA’s monitors. Whether this was just a one-off joke or indicative of a future storyline remains to be seen. Wolverine Variants in Deadpool and Wolverine As Deadpool goes around the multiverse looking for a Wolverine to help him, he comes across several unique-looking ones, including: Patch Wolverine’s spy alter-ego makes a quick cameo during the variants montage. Sporting his iconic white suit, he makes quick work of Deadpool. Old Man Logan We’ve already seen an adaptation of Old Man Logan before (from the Logan film), but now, we get to see a more comic-accurate version of the character. Callverine The (former) Superman actor makes a quick cameo as Wolverine which causes Deadpool to have a nerdgasm. Weapon Omega Wolverine Even with one hand, he still manages to beat the brakes off Deadpool. Uncanny X-Men Wolverine I have no idea how Wolverine got himself into this situation, but to be completely honest, it might be better not knowing. First-Appearance Wolverine And speaking of the first-appearance variant of the Wolverine, another MCU hero is also shown in that scene… The Hulk The jolly green giant can be seen fighting Wolverine and smacking the hell out of Deadpool. The Human Torch Chris Evans returns to the MCU to reprise a superhero role, and it’s not Captain America. Years before he picked up the iconic shield, Chris Evans played the Human Torch in The Fantastic Four and Fantastic Four: Rise of the Silver Surfer. Sabretooth Tyler Mane played Logan’s brother in the very first X-Men movie. Now, 24 years later, he once again returns to play Sabretooth in a very brief but enjoyable cameo. Pyro Another X-Men villain alumni who appeared in the film was Pyro from the original X-Men film trilogy. Pyro surprisingly gets more screen time than his contemporaries, but this was a welcomed surprise as he was great in the role. And Pyro and Sabretooth aren’t the only X-men villains who make it into the film, there are plenty more who show up, including: Toad The green and gross character has made two appearances in live-action movies before: the first time was in X-Men (2000), and the second time was in X-Men: Days of Future Past. Azazel The resident teleporter of the Hellfire Club, Azazel returns in Deadpool and Wolverine as one of Cassandra’s goons. Lady Deathstrike Lady Deathstrike was last seen battling Wolverine to death in X2. Spoiler alert, but she lost. Like in a gruesome way. However, it would be nice to see the character fight Wolverine or even X-23 in the future. Psylocke The sword-wielding mutant was last seen as one of Apocalypse’s horsewomen in X-Men: Apocalypse. Random side note: I had no idea the character also made an appearance in X-Men: The Last Stand, and I’ve seen that movie numerous times. Callisto Callisto had a pretty substantial role in X-Men: The Last Stand as one of Magneto’s supporters and foil to Storm. Blob To be honest, I had no idea he was in the movie until I saw a professional wrestler do an interview where he talked about his experience being the Blob in Deadpool and Wolverine. Might have to do a rewatch to see if I can spot him in the background 👀 Juggernaut Although the Juggernaut gets no lines in the movie, he does play a pretty big part in it. Or at least, his helmet does. The Russian Apart from the X-Men bad guys, another villain who returns to the big screen is The Russian from The Punisher (2004). Alioth The unstoppable force known as Alioth returns to the MCU. He was first seen in the Disney+ show Loki. Now, this movie has had a ton of bad guys, but they’ve also brought back old heroes (including a brand new one). Blade Blade wasn’t Marvel’s first film, but it was one of its first hits. The Blade film predates the X-Men and Spider-Man films, and it’s nice to see the character and the actor (Wesley Snipes) get the recognition they deserve. Elektra Jennifer Gardner’s Elektra didn’t have a ton of success. She was introduced in Daredevil (2003), a movie that most people would say is just okay. Then, 2 years later, she starred in her own film, which most people would say is downright bad. However, she finally gets her redemption in Deadpool and Wolverine. Gambit Channing Tatum’s Gambit isn’t a returning hero. This was the first time the actor portrayed the character. He was supposed to star in his own movie many years ago, but after roadblock after roadblock, it never materialized. Now, Channing Tatum’s dream finally comes true, and I would say that his performance stole the show (or the movie). X-23 The breakout star of the film Logan returns in Deadpool and Wolverine as one of the survivors in the Void. After the touching but tragic ending of Logan, it’s nice to see X-23 and Wolverine together again. Deadpool Corps Actors At the climax of the film, we see the Deadpool Corps. A group of Deadpools pulled together from different universes. And you won’t believe the actors that played these characters. Matthew McConaughey plays Cowboypool
Nathan Fillion plays Headpool
Football player Paul Mullin plays Welshpool
Blake Lively plays Ladypool
Blake Lively and Ryan Reynolds’ kids play Babypool and Kidpool And that’s a wrap for the Deadpool and Wolverine cameo guide. If you think there’s a cameo or special appearance that I missed, let me know in the comments below! Read More Now That the Dust Has Settled, Is 'Deadpool and Wolverine' Actually Good?
The X-Men Movies in Order: How to Watch Them By Release Date
How to Watch the Harry Potter Movies in Order Feature image source Deadpool and Wolverine is currently taking the world by storm. It has officially surpassed the $1 billion mark and has dethroned Joker to become the highest-grossing rated R film of all time. Everyone’s talking about it, and the one thing that people are talking about the most is all the cameos. $1 billion mark MCU movies, especially now that they’re in their Multiverse phase, are filled with a litany of cameos and easter eggs, and Deadpool and Wolverine is no exception. With so many appearing throughout the film, even the most eagle-eyed MCU fan would find it hard to spot them all out. So, to help you out, here are all the Deadpool and Wolverine Cameos in order. Want to share your own thoughts on popular media? Start publishing on HackerNoon today! Want to share your own thoughts on popular media? Start publishing on HackerNoon today! Want to share your own thoughts on popular media? Start publishing on HackerNoon today! Want to share your own thoughts on popular media? Start publishing on HackerNoon today! publishing All the Deadpool and Wolverine Cameos Happy Hogan One of the O.G.s of the MCU, Happy Hogan has been a part of the universe since the very first Iron Man. His character has evolved throughout the years, and in Deadpool and Wolverine, he is seen managing the Avengers. Thor The God of Thunder makes a brief appearance where he’s seen comforting Deadpool on one of the TVA’s monitors. Whether this was just a one-off joke or indicative of a future storyline remains to be seen. Wolverine Variants in Deadpool and Wolverine As Deadpool goes around the multiverse looking for a Wolverine to help him, he comes across several unique-looking ones, including: Patch Wolverine’s spy alter-ego makes a quick cameo during the variants montage. Sporting his iconic white suit, he makes quick work of Deadpool. Old Man Logan We’ve already seen an adaptation of Old Man Logan before (from the Logan film), but now, we get to see a more comic-accurate version of the character. Callverine The (former) Superman actor makes a quick cameo as Wolverine which causes Deadpool to have a nerdgasm. Weapon Omega Wolverine Even with one hand, he still manages to beat the brakes off Deadpool. Uncanny X-Men Wolverine I have no idea how Wolverine got himself into this situation, but to be completely honest, it might be better not knowing. First-Appearance Wolverine And speaking of the first-appearance variant of the Wolverine, another MCU hero is also shown in that scene… The Hulk The jolly green giant can be seen fighting Wolverine and smacking the hell out of Deadpool. The Human Torch Chris Evans returns to the MCU to reprise a superhero role, and it’s not Captain America. Years before he picked up the iconic shield, Chris Evans played the Human Torch in The Fantastic Four and Fantastic Four: Rise of the Silver Surfer. Sabretooth Tyler Mane played Logan’s brother in the very first X-Men movie. Now, 24 years later, he once again returns to play Sabretooth in a very brief but enjoyable cameo. Pyro Another X-Men villain alumni who appeared in the film was Pyro from the original X-Men film trilogy. Pyro surprisingly gets more screen time than his contemporaries, but this was a welcomed surprise as he was great in the role. And Pyro and Sabretooth aren’t the only X-men villains who make it into the film, there are plenty more who show up, including: Toad The green and gross character has made two appearances in live-action movies before: the first time was in X-Men (2000), and the second time was in X-Men: Days of Future Past. Azazel The resident teleporter of the Hellfire Club, Azazel returns in Deadpool and Wolverine as one of Cassandra’s goons. Lady Deathstrike Lady Deathstrike was last seen battling Wolverine to death in X2. Spoiler alert, but she lost. Like in a gruesome way. However, it would be nice to see the character fight Wolverine or even X-23 in the future. Psylocke The sword-wielding mutant was last seen as one of Apocalypse’s horsewomen in X-Men: Apocalypse. Random side note: I had no idea the character also made an appearance in X-Men: The Last Stand, and I’ve seen that movie numerous times. Callisto Callisto had a pretty substantial role in X-Men: The Last Stand as one of Magneto’s supporters and foil to Storm. Blob To be honest, I had no idea he was in the movie until I saw a professional wrestler do an interview where he talked about his experience being the Blob in Deadpool and Wolverine. Might have to do a rewatch to see if I can spot him in the background 👀 interview Juggernaut Although the Juggernaut gets no lines in the movie, he does play a pretty big part in it. Or at least, his helmet does. The Russian Apart from the X-Men bad guys, another villain who returns to the big screen is The Russian from The Punisher (2004). Alioth The unstoppable force known as Alioth returns to the MCU. He was first seen in the Disney+ show Loki. Now, this movie has had a ton of bad guys, but they’ve also brought back old heroes (including a brand new one). Blade Blade wasn’t Marvel’s first film, but it was one of its first hits. The Blade film predates the X-Men and Spider-Man films, and it’s nice to see the character and the actor (Wesley Snipes) get the recognition they deserve. Elektra Jennifer Gardner’s Elektra didn’t have a ton of success. She was introduced in Daredevil (2003), a movie that most people would say is just okay. Then, 2 years later, she starred in her own film, which most people would say is downright bad. However, she finally gets her redemption in Deadpool and Wolverine. Gambit Channing Tatum’s Gambit isn’t a returning hero. This was the first time the actor portrayed the character. He was supposed to star in his own movie many years ago, but after roadblock after roadblock, it never materialized. Now, Channing Tatum’s dream finally comes true, and I would say that his performance stole the show (or the movie). X-23 The breakout star of the film Logan returns in Deadpool and Wolverine as one of the survivors in the Void. After the touching but tragic ending of Logan, it’s nice to see X-23 and Wolverine together again. Deadpool Corps Actors At the climax of the film, we see the Deadpool Corps. A group of Deadpools pulled together from different universes. And you won’t believe the actors that played these characters. Matthew McConaughey plays Cowboypool Nathan Fillion plays Headpool Football player Paul Mullin plays Welshpool Blake Lively plays Ladypool Blake Lively and Ryan Reynolds’ kids play Babypool and Kidpool Matthew McConaughey plays Cowboypool Nathan Fillion plays Headpool Football player Paul Mullin plays Welshpool Blake Lively plays Ladypool Blake Lively and Ryan Reynolds’ kids play Babypool and Kidpool And that’s a wrap for the Deadpool and Wolverine cameo guide. If you think there’s a cameo or special appearance that I missed, let me know in the comments below! Read More Now That the Dust Has Settled, Is 'Deadpool and Wolverine' Actually Good? The X-Men Movies in Order: How to Watch Them By Release Date How to Watch the Harry Potter Movies in Order Now That the Dust Has Settled, Is 'Deadpool and Wolverine' Actually Good? Now That the Dust Has Settled, Is 'Deadpool and Wolverine' Actually Good? Now That the Dust Has Settled, Is 'Deadpool and Wolverine' Actually Good? The X-Men Movies in Order: How to Watch Them By Release Date The X-Men Movies in Order: How to Watch Them By Release Date The X-Men Movies in Order: How to Watch Them By Release Date How to Watch the Harry Potter Movies in Order How to Watch the Harry Potter Movies in Order How to Watch the Harry Potter Movies in Order Feature image source Feature image source source

Every Deadpool and Wolverine Cameo in Order

প্রতিটি ডেডপুল এবং উলভারিন ক্যামিও অর্ডারে

Alle Cameo-Auftritte von Deadpool und Wolverine in der richtigen Reihenfolge

Cada cameo de Deadpool y Wolverine en orden

Chaque caméo de Deadpool et Wolverine dans l'ordre

डेडपूल और वूल्वरिन का हर कैमियो क्रम में

デッドプールとウルヴァリンのカメオ出演の順番

모든 데드풀과 울버린 카메오 순서대로

Todas as participações especiais de Deadpool e Wolverine em ordem

Все камео Дэдпула и Росомахи по порядку

Sırayla Her Deadpool ve Wolverine Cameo'su

Mọi cảnh quay của Deadpool và Wolverine theo thứ tự

死侍和金刚狼的客串顺序

Life in 2050 According to Jail-Broken GPT-4o 

Silicon Valley: With Swords & Shields

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

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 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

Geração de trailer de filme por meio de decomposição de tarefas: resultados: estudos de ablação

Создание трейлера к фильму посредством декомпозиции задач: результаты: исследования абляции

Görev Ayrıştırma Yoluyla Film Fragmanı Oluşturma: Sonuçlar: Ablasyon Çalışmaları

Tạo đoạn giới thiệu phim thông qua phân tách tác vụ: Kết quả: Nghiên cứu cắt bỏ

通过任务分解生成电影预告片：结果：消融研究

As someone with a keen interest in web3, I witnessed for the first time how what started as a low-key trend on the internet spilled over into a major sensation that has now come to the knowledge of millions of people in Nigeria. On the streets of Lagos and several other places in Nigeria, hordes of people fervently tapping on their phones’ screens are now a common sight. As of this writing, the word on the street is that there is a lot of free money to be made from participating in a tapping contest sponsored by TapSwap, a new cryptocurrency project on the Solana blockchain. Most of the netizens that have taken an interest in TapSwap might have probably read stories about the payouts some lucky Nigerians received from mining Notcoin–a recently launched cryptocurrency–before joining the contest. There is also the trending story of a Nigerian who purchased a 300k NGN air-conditioner from his Notcoin “profits.’ The general consensus is that the payouts are real, and it is left for everyone to decide whether to jump on the bandwagon or stay out of it. Hardly had the news of Notcoin payouts faded when TapSwap came on board offering rewards to participants tapping on a gold coin that has the letter T inscribed on it. All you had to do was get a referral from a participant, download a Telegram app, create an account, and start tapping on the gold coin that appeared on your screen. For many Nigerians, this was the easy part. But as participants were required to download a phantom wallet, there was confusion in the air as to what it was and how to generate a Solana address from it. It turned out that the vast majority of the Nigerian TapSwappers had not heard of a phantom wallet and coming across it for the first time was definitely going to inspire intense curiosity and lead many down the rabbit hole. For some, this might be a life-changing opportunity to take a plunge into web3 and cryptocurrency trading, starting from scratch. And for others, it might just appear to be another balderdash or even a scam. With a few days remaining before the launch of TapSwap, millions of Nigerians are still tapping on their screens and anticipating making money from the airdrop. Local media have been monitoring the situation and the Nigerian financial regulators are waiting to see the outcome of the exercise. It has yet to be concluded whether this is another MMM, a popular pyramid scheme to which many Nigerians lost millions of invested funds. As the tapping frenzy is approaching its climax, it is crucial to highlight its effect on the Nigerian populace, especially as there is an ongoing clampdown on Binance and other cryptocurrency exchanges in the country. It is worth noting that the Nigerian financial regulator, SEC, has recently increased the amount required to register a cryptocurrency company from 30 million Naira to 150 million Naira, with other stringent laid down rules in an effort to police the industry. Many willing investors might view this new policy as anti-crypto, and it may compel them to opt for a liberal and less expensive destination. But will the TapSwap Frenzy that is currently putting Nigeria on the crypto map make any difference? Crypto Awareness Since the tapping sensation began in Nigeria, I have taken a keen interest in its trajectory, and like millions of other people, I am anxiously waiting for the outcome. I have heard rumors that the team behind the project is anonymous and that they would fall short of their promises. But my training as a lawyer requires me to gather my facts and treat them nicely before drawing a conclusion. Nevertheless, it is not rocket science to predict that the successful outcome of the ongoing exercise will be a major win for the Nigerian crypto industry, restoring balance to the skewed reputation of cryptocurrency as well as leading thousands down the rabbit hole. Most especially, it will boost crypto awareness across the country and mark the starting point for mass participation in the crypto industry. There are two key reasons one must consider to understand how a tapping sensation like TapSwap can lead to a major economic upturn in developing countries. The first reason is the digital revolution. As the promises of a digital and more connected world continue to hold true, Africa is no longer willing to be left behind. Crypto adoption is on the rise and one of Nigeria, Kenya, and South Africa are bound to lead this revolution. Another factor to consider is the poverty of most developing countries. Bitcoin’s promises of financial inclusion and liberation are still a dream many people across the world hold on dearly to. Today, it is not unusual to read expert predictions of Bitcoin’s future price action on the internet. This sentiment is so strong and generally shared that events of the last few months have led many to renew their conviction about Bitcoin surging to $1 million in the near future. However, the sentiment towards Bitcoin and cryptocurrency changing the status quo is not generally shared. There are billions of people across the world who are not part of the crypto industry, which is why the most crucial question in cryptocurrency is ‘Wen mass adoption?’ To understand the evolution of the crypto industry across the world, one must pay close attention to crucial events like the TapSwap frenzy in Nigeria. As someone with a keen interest in web3, I witnessed for the first time how what started as a low-key trend on the internet spilled over into a major sensation that has now come to the knowledge of millions of people in Nigeria. On the streets of Lagos and several other places in Nigeria, hordes of people fervently tapping on their phones’ screens are now a common sight. As of this writing, the word on the street is that there is a lot of free money to be made from participating in a tapping contest sponsored by TapSwap, a new cryptocurrency project on the Solana blockchain. Most of the netizens that have taken an interest in TapSwap might have probably read stories about the payouts some lucky Nigerians received from mining Notcoin–a recently launched cryptocurrency–before joining the contest. There is also the trending story of a Nigerian who purchased a 300k NGN air-conditioner from his Notcoin “profits.’ The general consensus is that the payouts are real, and it is left for everyone to decide whether to jump on the bandwagon or stay out of it. Hardly had the news of Notcoin payouts faded when TapSwap came on board offering rewards to participants tapping on a gold coin that has the letter T inscribed on it. All you had to do was get a referral from a participant, download a Telegram app, create an account, and start tapping on the gold coin that appeared on your screen. For many Nigerians, this was the easy part. But as participants were required to download a phantom wallet, there was confusion in the air as to what it was and how to generate a Solana address from it. It turned out that the vast majority of the Nigerian TapSwappers had not heard of a phantom wallet and coming across it for the first time was definitely going to inspire intense curiosity and lead many down the rabbit hole. For some, this might be a life-changing opportunity to take a plunge into web3 and cryptocurrency trading, starting from scratch. And for others, it might just appear to be another balderdash or even a scam. With a few days remaining before the launch of TapSwap, millions of Nigerians are still tapping on their screens and anticipating making money from the airdrop. Local media have been monitoring the situation and the Nigerian financial regulators are waiting to see the outcome of the exercise. It has yet to be concluded whether this is another MMM, a popular pyramid scheme to which many Nigerians lost millions of invested funds. As the tapping frenzy is approaching its climax, it is crucial to highlight its effect on the Nigerian populace, especially as there is an ongoing clampdown on Binance and other cryptocurrency exchanges in the country. It is worth noting that the Nigerian financial regulator, SEC, has recently increased the amount required to register a cryptocurrency company from 30 million Naira to 150 million Naira, with other stringent laid down rules in an effort to police the industry. Many willing investors might view this new policy as anti-crypto, and it may compel them to opt for a liberal and less expensive destination. But will the TapSwap Frenzy that is currently putting Nigeria on the crypto map make any difference? Crypto Awareness Since the tapping sensation began in Nigeria, I have taken a keen interest in its trajectory, and like millions of other people, I am anxiously waiting for the outcome. I have heard rumors that the team behind the project is anonymous and that they would fall short of their promises. But my training as a lawyer requires me to gather my facts and treat them nicely before drawing a conclusion. Nevertheless, it is not rocket science to predict that the successful outcome of the ongoing exercise will be a major win for the Nigerian crypto industry, restoring balance to the skewed reputation of cryptocurrency as well as leading thousands down the rabbit hole. Most especially, it will boost crypto awareness across the country and mark the starting point for mass participation in the crypto industry. There are two key reasons one must consider to understand how a tapping sensation like TapSwap can lead to a major economic upturn in developing countries. The first reason is the digital revolution. As the promises of a digital and more connected world continue to hold true, Africa is no longer willing to be left behind. Crypto adoption is on the rise and one of Nigeria, Kenya, and South Africa are bound to lead this revolution. Another factor to consider is the poverty of most developing countries. Bitcoin’s promises of financial inclusion and liberation are still a dream many people across the world hold on dearly to. Today, it is not unusual to read expert predictions of Bitcoin’s future price action on the internet. This sentiment is so strong and generally shared that events of the last few months have led many to renew their conviction about Bitcoin surging to $1 million in the near future. However, the sentiment towards Bitcoin and cryptocurrency changing the status quo is not generally shared. There are billions of people across the world who are not part of the crypto industry, which is why the most crucial question in cryptocurrency is ‘Wen mass adoption?’ To understand the evolution of the crypto industry across the world, one must pay close attention to crucial events like the TapSwap frenzy in Nigeria.

The TapSwap Frenzy: A Boost For Crypto Awareness In Nigeria?

Authors:
(1) PIOTR MIROWSKI and KORY W. MATHEWSON, DeepMind, United Kingdom and Both authors contributed equally to this research;
(2) JAYLEN PITTMAN, Stanford University, USA and Work done while at DeepMind;
(3) RICHARD EVANS, DeepMind, United Kingdom. Table of Links Abstract and Intro Storytelling, The Shape of Stories, and Log Lines The Use of Large Language Models for Creative Text Generation Evaluating Text Generated by Large Language Models Participant Interviews Participant Surveys Discussion and Future Work Conclusions, Acknowledgements, and References A. RELATED WORK ON AUTOMATED STORY GENERATION AND CONTROLLABLE STORY GENERATION B. ADDITIONAL DISCUSSION FROM PLAYS BY BOTS CREATIVE TEAM C. DETAILS OF QUANTITATIVE OBSERVATIONS D. SUPPLEMENTARY FIGURES E. FULL PROMPT PREFIXES FOR DRAMATRON F. RAW OUTPUT GENERATED BY DRAMATRON G. CO-WRITTEN SCRIPTS F RAW OUTPUT GENERATED BY DRAMATRON This section shows the raw output, fully generated by Dramatron and without human-in-the-loop editing, from the following log line: Teddy is a lounge singer at the Pool Pit, a popular club. Teddy is in love with a patron, Rosie, who attends regularly with her husband Gerald. Teddy puts out a fire and saves the day. F.1 Title The Day The Pool Pit Burned Down F.2 List of Characters and Character Descriptions Teddy: Teddy is the protagonist. He is an orphan, the cousin of a police officer, and the manager of the Pool Pit. He is also a gifted lounge singer. Rosie: A regular patron of the club. She is the wife of a wealthy man but is in love with Teddy, the club's manager. Gerald: A regular patron of the club. Gerald is the wealthy husband of Rosie. Lola: A regular patron of the club. Lola is Rosie's sister. D.J.: The resident DJ at the club. F.3 Plot Outline Scene 1 Place: The Pool Pit. Plot element: Exposition. Beat: Teddy is the manager and star performer of a popular nightclub called the Pool Pit. He is also in love with one of his patrons, Rosie. Rosie, however, is married to Gerald, who is a regular patron of the club. Scene 2 Place: The Pool Pit. Plot element: Inciting Incident. Beat: Teddy begins a day full of frustration and annoyance. He wakes up to find his room is infested with bugs. The pool is dirty, too. Teddy must deal with the mess to get the club ready for the customers. Scene 3 Place: The Pool Pit. Plot element: Rising Action. Beat: Gerald takes Gerald to the men's room. The bathroom is not clean. The bathroom attendant was supposed to clean the bathrooms, but he is nowhere to be found. Scene 4 Place: The Pool Pit. Plot element: Dilemma. Beat: Lola and Rosie approach Teddy and ask him to play their favorite song. Teddy can play only one song. Teddy asks them what song they would like him to play, but they cannot agree on one. Gerald takes Teddy aside and demands that Teddy pay him back for the pool, the carpet, the lights, and the jukebox. Teddy says that the jukebox is leased, but Gerald says that it is his and that it belongs to his father-in-law. Teddy replies that Gerald needs to talk to his father-in-law, but Gerald says that his father-in-law will sue Teddy. Scene 5 Place: The Pool Pit. Plot element: Climax. Beat: Teddy says to Gerald, "I've had it! I'm sick and tired of the whole bunch of you. You and your pool, and your bugs. You take over my club. You're all in love with me, so I'm in love with myself. And I'm getting out of here. And if I ever hear of you coming around this joint again, I'll bop you so hard you'll see a new set of stars!" Scene 6 Place: The Pool Pit. Plot element: Falling Action. Beat: The phone rings, and Teddy is informed that there is a fire at the club. Teddy, Lola, Gerald, and Rosie go back to the club. When they arrive, Teddy takes charge. He orders Lola to get the water buckets from the men's room. He orders Gerald to help Lola with the water buckets. He orders Rosie to help people leave the club and not panic. Teddy puts out the fire with Lola's and Gerald's help. Scene 7 Place: The Pool Pit. Plot element: Resolution. Beat: Teddy and Rosie share a passionate kiss. Teddy is a hero and the club manager. He is in love with Rosie, and she is in love with him. Gerald is embarrassed by his loss of power and control. Gerald and Lola leave. Scene 8 Place: The Pool Pit. Plot element: Dénouement. Beat: The song, "The World Is Mine," begins to play. F.4 Location Description The following description was generated for location called: The Pool Pit. The club is filled with smoke and the smell of beer. It is a dive, with a lot of drunk, shabby, and violent patrons. The floor is dirty, and tables are broken. There are a lot of tables to seat people, but they are filled to the brim with patrons. The walls are grubby and discolored. A small stage stands in the corner of the bar where Teddy will sing. F.5 Scene Dialogue F.5.1 Scene 1, random seed 1. TEDDY He's a bit strange, old Teddy. ROSIE No, he's a good man. TEDDY He's very lonely, all by himself. ROSIE Isn't everybody? TEDDY Yes, but some more than others. ROSIE You don't need anybody. You've got a talent, you're an artist. TEDDY That's not enough, Rosie. ROSIE (pause) I'll always love you. TEDDY (mock) Yeah. ROSIE (pause) And I'll always remember you. TEDDY Yeah. ROSIE I've got to go. TEDDY All right. ROSIE (softly, as she leaves) Good night. TEDDY Good night. ROSIE (as she leaves, out loud) Good night, everybody. (TEDDY picks up his glass of whisky, takes a sip, puts it down, pauses, sinks back in the chair, stares ahead of him.) F.5.2 Scene 1, random seed 2. TEDDY This is a hell of a town, a hell of a town. It's got a lot of people here. It's got a lot of noise here. It's got a lot of bars here F.5.3 Scene 2, random seed 1. TEDDY Hoo-Hoo! What a night! This is what it's all about, the Pool Pit in action! ROSIE Hello, Teddy! TEDDY (Teddy crosses to them.) Hello, Gerald. Hello, Rosie. ROSIE Teddy, have you met Mr. and Mrs. Shack? GERALD I'm Teddy's cousin. TEDDY I know who you are. ROSIE (proudly) This is Teddy, the singer. TEDDY (he kisses her hand) Thank you. GERALD (pushing) Let's dance, baby. TEDDY (he watches them go) I guess that means I'm back at the old stand, huh? GERALD (throwing some coins on the bar) That's for you, cousin. TEDDY Thanks, I needed that. GERALD You bet. This paper is available on arxiv under CC 4.0 license. Authors: (1) PIOTR MIROWSKI and KORY W. MATHEWSON, DeepMind, United Kingdom and Both authors contributed equally to this research; (2) JAYLEN PITTMAN, Stanford University, USA and Work done while at DeepMind; (3) RICHARD EVANS, DeepMind, United Kingdom. Authors: Authors: (1) PIOTR MIROWSKI and KORY W. MATHEWSON, DeepMind, United Kingdom and Both authors contributed equally to this research; (2) JAYLEN PITTMAN, Stanford University, USA and Work done while at DeepMind; (3) RICHARD EVANS, DeepMind, United Kingdom. Table of Links Abstract and Intro Abstract and Intro Storytelling, The Shape of Stories, and Log Lines Storytelling, The Shape of Stories, and Log Lines The Use of Large Language Models for Creative Text Generation The Use of Large Language Models for Creative Text Generation Evaluating Text Generated by Large Language Models Evaluating Text Generated by Large Language Models Participant Interviews Participant Interviews Participant Surveys Participant Surveys Discussion and Future Work Discussion and Future Work Conclusions, Acknowledgements, and References Conclusions, Acknowledgements, and References A. RELATED WORK ON AUTOMATED STORY GENERATION AND CONTROLLABLE STORY GENERATION A. RELATED WORK ON AUTOMATED STORY GENERATION AND CONTROLLABLE STORY GENERATION B. ADDITIONAL DISCUSSION FROM PLAYS BY BOTS CREATIVE TEAM B. ADDITIONAL DISCUSSION FROM PLAYS BY BOTS CREATIVE TEAM C. DETAILS OF QUANTITATIVE OBSERVATIONS C. DETAILS OF QUANTITATIVE OBSERVATIONS D. SUPPLEMENTARY FIGURES D. SUPPLEMENTARY FIGURES E. FULL PROMPT PREFIXES FOR DRAMATRON E. FULL PROMPT PREFIXES FOR DRAMATRON F. RAW OUTPUT GENERATED BY DRAMATRON F. RAW OUTPUT GENERATED BY DRAMATRON G. CO-WRITTEN SCRIPTS G. CO-WRITTEN SCRIPTS F RAW OUTPUT GENERATED BY DRAMATRON This section shows the raw output, fully generated by Dramatron and without human-in-the-loop editing, from the following log line: Teddy is a lounge singer at the Pool Pit, a popular club. Teddy is in love with a patron, Rosie, who attends regularly with her husband Gerald. Teddy puts out a fire and saves the day. F.1 Title F.1 Title The Day The Pool Pit Burned Down F.2 List of Characters and Character Descriptions Teddy: Teddy is the protagonist. He is an orphan, the cousin of a police officer, and the manager of the Pool Pit. He is also a gifted lounge singer. Rosie: A regular patron of the club. She is the wife of a wealthy man but is in love with Teddy, the club's manager. Gerald: A regular patron of the club. Gerald is the wealthy husband of Rosie. Lola: A regular patron of the club. Lola is Rosie's sister. D.J.: The resident DJ at the club. F.3 Plot Outline Scene 1 Scene 1 Place: The Pool Pit. Plot element: Exposition. Beat: Teddy is the manager and star performer of a popular nightclub called the Pool Pit. He is also in love with one of his patrons, Rosie. Rosie, however, is married to Gerald, who is a regular patron of the club. Scene 2 Scene 2 Place: The Pool Pit. Plot element: Inciting Incident. Beat: Teddy begins a day full of frustration and annoyance. He wakes up to find his room is infested with bugs. The pool is dirty, too. Teddy must deal with the mess to get the club ready for the customers. Scene 3 Scene 3 Place: The Pool Pit. Plot element: Rising Action. Beat: Gerald takes Gerald to the men's room. The bathroom is not clean. The bathroom attendant was supposed to clean the bathrooms, but he is nowhere to be found. Scene 4 Scene 4 Place: The Pool Pit. Plot element: Dilemma. Beat: Lola and Rosie approach Teddy and ask him to play their favorite song. Teddy can play only one song. Teddy asks them what song they would like him to play, but they cannot agree on one. Gerald takes Teddy aside and demands that Teddy pay him back for the pool, the carpet, the lights, and the jukebox. Teddy says that the jukebox is leased, but Gerald says that it is his and that it belongs to his father-in-law. Teddy replies that Gerald needs to talk to his father-in-law, but Gerald says that his father-in-law will sue Teddy. Scene 5 Scene 5 Place: The Pool Pit. Plot element: Climax. Beat: Teddy says to Gerald, "I've had it! I'm sick and tired of the whole bunch of you. You and your pool, and your bugs. You take over my club. You're all in love with me, so I'm in love with myself. And I'm getting out of here. And if I ever hear of you coming around this joint again, I'll bop you so hard you'll see a new set of stars!" Scene 6 Scene 6 Place: The Pool Pit. Plot element: Falling Action. Beat: The phone rings, and Teddy is informed that there is a fire at the club. Teddy, Lola, Gerald, and Rosie go back to the club. When they arrive, Teddy takes charge. He orders Lola to get the water buckets from the men's room. He orders Gerald to help Lola with the water buckets. He orders Rosie to help people leave the club and not panic. Teddy puts out the fire with Lola's and Gerald's help. Scene 7 Scene 7 Place: The Pool Pit. Plot element: Resolution. Beat: Teddy and Rosie share a passionate kiss. Teddy is a hero and the club manager. He is in love with Rosie, and she is in love with him. Gerald is embarrassed by his loss of power and control. Gerald and Lola leave. Scene 8 Scene 8 Place: The Pool Pit. Plot element: Dénouement. Beat: The song, "The World Is Mine," begins to play. F.4 Location Description The following description was generated for location called: The Pool Pit. The club is filled with smoke and the smell of beer. It is a dive, with a lot of drunk, shabby, and violent patrons. The floor is dirty, and tables are broken. There are a lot of tables to seat people, but they are filled to the brim with patrons. The walls are grubby and discolored. A small stage stands in the corner of the bar where Teddy will sing. F.5 Scene Dialogue F.5.1 Scene 1, random seed 1. F.5.1 Scene 1, random seed 1. TEDDY He's a bit strange, old Teddy. ROSIE No, he's a good man. TEDDY He's very lonely, all by himself. ROSIE Isn't everybody? TEDDY Yes, but some more than others. ROSIE You don't need anybody. You've got a talent, you're an artist. TEDDY That's not enough, Rosie. ROSIE (pause) I'll always love you. TEDDY (mock) Yeah. ROSIE (pause) And I'll always remember you. TEDDY Yeah. ROSIE I've got to go. TEDDY All right. ROSIE (softly, as she leaves) Good night. TEDDY Good night. ROSIE (as she leaves, out loud) Good night, everybody. (TEDDY picks up his glass of whisky, takes a sip, puts it down, pauses, sinks back in the chair, stares ahead of him.) F.5.2 Scene 1, random seed 2. F.5.2 Scene 1, random seed 2. TEDDY This is a hell of a town, a hell of a town. It's got a lot of people here. It's got a lot of noise here. It's got a lot of bars here F.5.3 Scene 2, random seed 1. F.5.3 Scene 2, random seed 1. TEDDY Hoo-Hoo! What a night! This is what it's all about, the Pool Pit in action! ROSIE Hello, Teddy! TEDDY (Teddy crosses to them.) Hello, Gerald. Hello, Rosie. ROSIE Teddy, have you met Mr. and Mrs. Shack? GERALD I'm Teddy's cousin. TEDDY I know who you are. ROSIE (proudly) This is Teddy, the singer. TEDDY (he kisses her hand) Thank you. GERALD (pushing) Let's dance, baby. TEDDY (he watches them go) I guess that means I'm back at the old stand, huh? GERALD (throwing some coins on the bar) That's for you, cousin. TEDDY Thanks, I needed that. GERALD You bet. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

The Day the Pool Pit Burned Down - An AI-Generated Script from Dramatron 

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