MovieSum: An Abstractive Summarization Dataset for Movie Screenplays
Authors: name: Rohit Saxena, name: Frank Keller

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.