Summary of Moviesum: An Abstractive Summarization Dataset For Movie Screenplays, by Rohit Saxena et al.
MovieSum: An Abstractive Summarization Dataset for Movie Screenplays
by Rohit Saxena, Frank Keller
First submitted to arxiv on: 12 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a new dataset, MovieSum, for abstractive summarization of movie screenplays. This dataset comprises 2200 movie screenplays accompanied by their Wikipedia plot summaries. Large language models have shown advancements in document summarization, but struggle with processing long input contexts, making movie screenplay summarization challenging. The authors manually formatted the movie screenplays to represent their structural elements and provide metadata with IMDb IDs for access to additional external knowledge. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MovieSum is a new dataset designed for summarizing movie screenplays. It includes 2200 movie screenplays along with their Wikipedia plot summaries. This helps researchers understand how to summarize longer texts, like movie scripts. The authors made sure the data looks like real movie screenplays and added extra information, like IMDb IDs, to make it easier to use. |
Keywords
» Artificial intelligence » Summarization
