What Makes a Scene Important? This AI Knows

Authors: (1) Rohit Saxena, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburg; (2) RFrank Keller, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburg. Table of Links Part 1 Part 2 Part 3 Part 4 Part 5 Part 6 6. Automatic QA-based Evaluation Metrics like ROUGE (lexically based) and BERTScore (embedding based) are good for comparing the topic similarity between the reference and generated summaries, but fail to compare content-based factual consistency. To further evaluate the performance of our model, we used QAEval (Deutsch et al., 2021), a question-answering-based evaluation that generates question-answer pairs using the reference summaries. It then uses the modelgenerated summaries (candidates) to answer these questions, thereby measuring information overlap. It reports two standard answer verification methods used by SQuAD, F1 and exact match (EM) (Rajpurkar et al., 2016), averaged over all questions for all model-generated summaries. Before the final evaluation, we filtered the generated questions using a question filtering method similar to Fabbri et al. (2022), which is useful for removing spurious questions/answers (for example answers consisting of personal pronouns and wh-pronouns). Table 6 shows results for QAEval on summaries generated by models using full text input, the two-stage heuristic approach (Pu et al., 2022), and Select and Summarize (our model). We find that Select and Summarize performs better in answering factual questions, with a mean F1 of 29.42 and a mean exact match of 20.05%. Our model shows a clear improvement over using full movie scripts or a two-stage heuristic approach. 7. Zero-Shot on SummScreen-FD We further investigate the performance of the scene saliency classifier on SummScreenFD (Chen et al., 2022) as used in SCROLLS benchmark (Shaham et al., 2022). SummScreenFD consists of transcripts of TV show episodes with human-written recaps. We performed a zero-shot classification of the salient scenes on the SummScreenFD and used only salient scenes to fine-tune LED for summarization. We compare the results with state-of-the-art methods on the dataset and report ROUGE scores. We observe that our model achieves comparable results to the state of the art on the SummScreenFD dataset as shown in Table 7, but with fewer parameters (Ivgi et al., 2023; Zhong et al., 2022). 8. Discussion and Conclusion In this paper, we introduced a dataset of 100 movies in which movie plot summaries are manually aligned with scenes in the corresponding movie script. Our dataset can be used to evaluate content selection strategies and extractive summarization for movie scripts. Using this dataset, we proposed a scene saliency classification model for the automatic identification of salient scenes in a movie script and introduced an abstractive summarization model that only uses the salient scenes to generate the movie summary. Our experiments showed that the proposed model achieves a significant improvement over the previous state of the art on the Scriptbase corpus for movie script summarization and performs comparable to the state of the art on the SummScreenFD dataset using zero-shot salient scene detection. Our work demonstrates that the output of a summarization model can improve when content selection is performed (by using only the salient scenes). A good content selection strategy can in principle reduce the input size without compromising the quality of the generated output. As a result of the smaller input size, the computational and memory requirements of the underlying large language model can be significantly reduced. Limitations Limitations of this work include that we defined the saliency of a scene as recall in user-written summaries. However, there are many aspects that can make a scene salient, including the presence of an important character or event in the scene, or just the fact that the scene is visually stunning. These factors can be explored in future work. Also, we discovered that many of the movie scripts in the Scriptbase corpus are not the final production scripts, which means they are different from the final movie as it was released. This imposes a limit on the quality of the summary that can be generated from a script. Our current model works in a pipeline of salient scene classification and then uses these scenes to summarize the movie. This means that it can propagate salience classification errors into the summarization step. Human evaluation of summaries generated from long-form text is challenging, as it requires human evaluators to read very long texts such as movie scripts. Therefore, future work is required to evaluate automatically generated movie summaries. Ethics Statement Large Language Models: This paper uses pretrained large language models, which have been shown to be subject to a variety of biases, to occasionally generate toxic language, and to hallucinate content. Therefore, the summaries generated by our approach should not be released without automatic filtering or manual checking. Experimental Participants: The departmental ethics panel judged our human annotation study to be exempt from ethical approval, as all participants were employees of the University of Edinburgh, and as such were protected by employment law. Nevertheless, annotators were given a participant information sheet before they started work. They were also informed about the age rating of each movie script, based on which they could decide whether they want to annotate this script or not. Participants were paid at the standard hourly rate for tutors and demonstrators at the university. Acknowledgements This work was supported in part by the UKRI Centre for Doctoral Training in Natural Language Processing, funded by UK Research and Innovation (grant EP/S022481/1), Huawei, and the School of Informatics at the University of Edinburgh. We would like to thank the anonymous reviewers for their helpful feedback. References Divyansh Agarwal, Alexander R. Fabbri, Simeng Han, Wojciech Kryscinski, Faisal Ladhak, Bryan Li, Kathleen McKeown, Dragomir Radev, Tianyi Zhang, and Sam Wiseman. 2022. CREATIVESUMM: Shared task on automatic summarization for creative writing. In Proceedings of The Workshop on Automatic Summarization for Creative Writing, pages 67–73, Gyeongju, Republic of Korea. Association for Computational Linguistics. Iz Beltagy, Matthew E. Peters, and Arman Cohan. 2020. Longformer: The long-document transformer. arXiv:2004.05150. Amanda Bertsch, Uri Alon, Graham Neubig, and Matthew Gormley. 2023. Unlimiformer: Long-range transformers with unlimited length input. In Advances in Neural Information Processing Systems, volume 36, pages 35522–35543. Curran Associates, Inc. Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020. Language models are few-shot learners. In Advances in Neural Information Processing Systems, volume 33, pages 1877–1901. Curran Associates, Inc. Mingda Chen, Zewei Chu, Sam Wiseman, and Kevin Gimpel. 2022. SummScreen: A dataset for abstractive screenplay summarization. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 8602–8615, Dublin, Ireland. Association for Computational Linguistics. Yen-Chun Chen and Mohit Bansal. 2018. Fast abstractive summarization with reinforce-selected sentence rewriting. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 675–686, Melbourne, Australia. Association for Computational Linguistics. Jianpeng Cheng and Mirella Lapata. 2016. Neural summarization by extracting sentences and words. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 484–494, Berlin, Germany. Association for Computational Linguistics. Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, et al. 2022. 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Chenguang Zhu, Ziyi Yang, Robert Gmyr, Michael Zeng, and Xuedong Huang. 2021b. Leveraging lead bias for zero-shot abstractive news summarization. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’21, page 1462–1471, New York, NY, USA. Association for Computing Machinery. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Rohit Saxena, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburg; (2) RFrank Keller, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburg. Authors: Authors: (1) Rohit Saxena, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburg; (2) RFrank Keller, Institute for Language, Cognition and Computation, School of Informatics, University of Edinburg. Table of Links Part 1 Part 1 Part 2 Part 2 Part 3 Part 3 Part 4 Part 4 Part 5 Part 5 Part 6 Part 6 6. Automatic QA-based Evaluation Metrics like ROUGE (lexically based) and BERTScore (embedding based) are good for comparing the topic similarity between the reference and generated summaries, but fail to compare content-based factual consistency. To further evaluate the performance of our model, we used QAEval (Deutsch et al., 2021), a question-answering-based evaluation that generates question-answer pairs using the reference summaries. It then uses the modelgenerated summaries (candidates) to answer these questions, thereby measuring information overlap. It reports two standard answer verification methods used by SQuAD, F1 and exact match (EM) (Rajpurkar et al., 2016), averaged over all questions for all model-generated summaries. Before the final evaluation, we filtered the generated questions using a question filtering method similar to Fabbri et al. (2022), which is useful for removing spurious questions/answers (for example answers consisting of personal pronouns and wh-pronouns). Table 6 shows results for QAEval on summaries generated by models using full text input, the two-stage heuristic approach (Pu et al., 2022), and Select and Summarize (our model). We find that Select and Summarize performs better in answering factual questions, with a mean F1 of 29.42 and a mean exact match of 20.05%. Our model shows a clear improvement over using full movie scripts or a two-stage heuristic approach. 7. Zero-Shot on SummScreen-FD We further investigate the performance of the scene saliency classifier on SummScreenFD (Chen et al., 2022) as used in SCROLLS benchmark (Shaham et al., 2022). SummScreenFD consists of transcripts of TV show episodes with human-written recaps. We performed a zero-shot classification of the salient scenes on the SummScreenFD and used only salient scenes to fine-tune LED for summarization. We compare the results with state-of-the-art methods on the dataset and report ROUGE scores. We observe that our model achieves comparable results to the state of the art on the SummScreenFD dataset as shown in Table 7, but with fewer parameters (Ivgi et al., 2023; Zhong et al., 2022). 8. Discussion and Conclusion In this paper, we introduced a dataset of 100 movies in which movie plot summaries are manually aligned with scenes in the corresponding movie script. Our dataset can be used to evaluate content selection strategies and extractive summarization for movie scripts. Using this dataset, we proposed a scene saliency classification model for the automatic identification of salient scenes in a movie script and introduced an abstractive summarization model that only uses the salient scenes to generate the movie summary. Our experiments showed that the proposed model achieves a significant improvement over the previous state of the art on the Scriptbase corpus for movie script summarization and performs comparable to the state of the art on the SummScreenFD dataset using zero-shot salient scene detection. Our work demonstrates that the output of a summarization model can improve when content selection is performed (by using only the salient scenes). A good content selection strategy can in principle reduce the input size without compromising the quality of the generated output. As a result of the smaller input size, the computational and memory requirements of the underlying large language model can be significantly reduced. Limitations Limitations of this work include that we defined the saliency of a scene as recall in user-written summaries. However, there are many aspects that can make a scene salient, including the presence of an important character or event in the scene, or just the fact that the scene is visually stunning. These factors can be explored in future work. Also, we discovered that many of the movie scripts in the Scriptbase corpus are not the final production scripts, which means they are different from the final movie as it was released. This imposes a limit on the quality of the summary that can be generated from a script. Our current model works in a pipeline of salient scene classification and then uses these scenes to summarize the movie. This means that it can propagate salience classification errors into the summarization step. Human evaluation of summaries generated from long-form text is challenging, as it requires human evaluators to read very long texts such as movie scripts. Therefore, future work is required to evaluate automatically generated movie summaries. Ethics Statement Large Language Models: This paper uses pretrained large language models, which have been shown to be subject to a variety of biases, to occasionally generate toxic language, and to hallucinate content. Therefore, the summaries generated by our approach should not be released without automatic filtering or manual checking. Large Language Models: Experimental Participants: The departmental ethics panel judged our human annotation study to be exempt from ethical approval, as all participants were employees of the University of Edinburgh, and as such were protected by employment law. Nevertheless, annotators were given a participant information sheet before they started work. They were also informed about the age rating of each movie script, based on which they could decide whether they want to annotate this script or not. Participants were paid at the standard hourly rate for tutors and demonstrators at the university. Experimental Participants: Acknowledgements This work was supported in part by the UKRI Centre for Doctoral Training in Natural Language Processing, funded by UK Research and Innovation (grant EP/S022481/1), Huawei, and the School of Informatics at the University of Edinburgh. We would like to thank the anonymous reviewers for their helpful feedback. References Divyansh Agarwal, Alexander R. Fabbri, Simeng Han, Wojciech Kryscinski, Faisal Ladhak, Bryan Li, Kathleen McKeown, Dragomir Radev, Tianyi Zhang, and Sam Wiseman. 2022. CREATIVESUMM: Shared task on automatic summarization for creative writing. In Proceedings of The Workshop on Automatic Summarization for Creative Writing, pages 67–73, Gyeongju, Republic of Korea. Association for Computational Linguistics. Iz Beltagy, Matthew E. Peters, and Arman Cohan. 2020. Longformer: The long-document transformer. arXiv:2004.05150. Amanda Bertsch, Uri Alon, Graham Neubig, and Matthew Gormley. 2023. Unlimiformer: Long-range transformers with unlimited length input. In Advances in Neural Information Processing Systems, volume 36, pages 35522–35543. Curran Associates, Inc. Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020. Language models are few-shot learners. In Advances in Neural Information Processing Systems, volume 33, pages 1877–1901. Curran Associates, Inc. Mingda Chen, Zewei Chu, Sam Wiseman, and Kevin Gimpel. 2022. SummScreen: A dataset for abstractive screenplay summarization. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 8602–8615, Dublin, Ireland. Association for Computational Linguistics. Yen-Chun Chen and Mohit Bansal. 2018. Fast abstractive summarization with reinforce-selected sentence rewriting. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 675–686, Melbourne, Australia. Association for Computational Linguistics. Jianpeng Cheng and Mirella Lapata. 2016. Neural summarization by extracting sentences and words. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 484–494, Berlin, Germany. Association for Computational Linguistics. Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, et al. 2022. Scaling instruction-finetuned language models. arXiv preprint arXiv:2210.11416. Arman Cohan, Franck Dernoncourt, Doo Soon Kim, Trung Bui, Seokhwan Kim, Walter Chang, and Nazli Goharian. 2018. A discourse-aware attention model for abstractive summarization of long documents. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 615–621, New Orleans, Louisiana. Association for Computational Linguistics. Daniel Deutsch, Tania Bedrax-Weiss, and Dan Roth. 2021. Towards question-answering as an automatic metric for evaluating the content quality of a summary. Transactions of the Association for Computational Linguistics, 9:774–789. Zi-Yi Dou, Pengfei Liu, Hiroaki Hayashi, Zhengbao Jiang, and Graham Neubig. 2021. GSum: A general framework for guided neural abstractive summarization. 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