MovieSum: An Abstractive Summarization Dataset for Movie Screenplays

TL;DR

MovieSum introduces a new, large dataset of movie screenplays and summaries to advance research in abstractive screenplay summarization, addressing challenges of long input processing and structural understanding.

Contribution

The paper presents MovieSum, a novel dataset of 2200 movie screenplays with summaries, metadata, and structural formatting, enabling improved research in long-document summarization.

Findings

Large language models perform baseline summarization on MovieSum.

MovieSum is larger and more detailed than previous datasets.

Metadata facilitates access to external knowledge.

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.