title: MovieSum: An Abstractive Summarization Dataset for Movie Screenplays
summary: 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.
paper_authors._id: 66bc35255ee1c61552bccf46
paper_authors.name: Rohit Saxena
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-08-14T08:11:24.657Z
paper_authors._id: 657ccbf2869d5bb0e53b482f
paper_authors.avatarUrl: /avatars/2eae5a10bdc14814a04d9f255f16de6b.svg
paper_authors.fullname: Rohit Saxena
paper_authors.type: user
paper_authors.user: rohitsaxena
paper_authors._id: 66bc35255ee1c61552bccf47
paper_authors.name: Frank Keller
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-08-14T11:05:48.721Z
paper_authors._id: 632f501c310d1b5d94a5b714
paper_authors.avatarUrl: /avatars/e835860128623a40de065f247f33262e.svg
paper_authors.fullname: Frank Keller
paper_authors.type: user
paper_authors.user: kellerfe
paper_title: MovieSum: An Abstractive Summarization Dataset for Movie Screenplays
paper_summary: 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.