{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/moviesum-an-abstractive-summarization-dataset","title":"MovieSum: An Abstractive Summarization Dataset for Movie Screenplays","arxiv_id":"2408.06281","date":"2024-08-12","proceeding":null,"authors":["Rohit Saxena","Frank Keller"],"abstract":"Movie screenplay summarization is challenging, as it requires an understanding of long input contexts and various elements unique to movies. Large language models have shown significant advancements in document summarization, but they often struggle with processing long input contexts. Furthermore, while television transcripts have received attention in recent studies, movie screenplay summarization remains underexplored. To stimulate research in this area, we present a new dataset, MovieSum, for abstractive summarization of movie screenplays. This dataset comprises 2200 movie screenplays accompanied by their Wikipedia plot summaries. We manually formatted the movie screenplays to represent their structural elements. Compared to existing datasets, MovieSum possesses several distinctive features: (1) It includes movie screenplays, which are longer than scripts of TV episodes. (2) It is twice the size of previous movie screenplay datasets. (3) It provides metadata with IMDb IDs to facilitate access to additional external knowledge. We also show the results of recently released large language models applied to summarization on our dataset to provide a detailed baseline.","url_abs":"https://arxiv.org/abs/2408.06281v1","url_pdf":"https://arxiv.org/pdf/2408.06281v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"moviesum-an-abstractive-summarization-dataset","repo_url":"https://github.com/saxenarohit/moviesum","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"abstractive-text-summarization","task_name":"Abstractive Text Summarization"},{"task_slug":"document-summarization","task_name":"Document Summarization"},{"task_slug":"long-form-narrative-summarization","task_name":"Long-Form Narrative Summarization"}],"methods":[{"method_slug":"attention","method_name":"Attention"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/long-form-narrative-summarization-on-moviesum","task":"Long-Form Narrative Summarization","dataset":"MovieSum","model":"Description Only (LED)","rank_in_archive_order":3,"of":5,"metrics":{"BERTScore (F1)":"58.92"},"uses_additional_data":false},{"leaderboard":"/sota/long-form-narrative-summarization-on-moviesum","task":"Long-Form Narrative Summarization","dataset":"MovieSum","model":"Two-Stage Heuristic (LED Large)","rank_in_archive_order":4,"of":5,"metrics":{"BERTScore (F1)":"58.54"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2408.06281","atlas_url":"https://app.syntology.ai/?focus=2408.06281","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}