Screenplay Summarization Using Latent Narrative Structure

Pinelopi Papalampidi, Frank Keller, Lea Frermann, Mirella Lapata

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Most general-purpose extractive summarization models are trained on news articles, which are short and present all important information upfront. As a result, such models are biased on position and often perform a smart selection of sentences from the beginning of the document. When summarizing long narratives, which have complex structure and present information piecemeal, simple position heuristics are not sufficient. In this paper, we propose to explicitly incorporate the underlying structure of narratives into general unsupervised and supervised extractive summarization models. We formalize narrative structure in terms of key narrative events (turning points) and treat it as latent in order to summarize screenplays (i.e., extract an optimal sequence of scenes). Experimental results on the CSI corpus of TV screenplays, which we augment with scene-level summarization labels, show that latent turning points correlate with important aspects of a CSI episode and improve summarization performance over general extractive algorithms leading to more complete and diverse summaries.
Original languageEnglish
Title of host publicationProceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Place of PublicationOnline
PublisherAssociation for Computational Linguistics
Number of pages14
ISBN (Electronic)978-1-952148-25-5
Publication statusPublished - 10 Jul 2020
Event2020 Annual Conference of the Association for Computational Linguistics - Hyatt Regency Seattle, Virtual conference, United States
Duration: 5 Jul 202010 Jul 2020
Conference number: 58


Conference2020 Annual Conference of the Association for Computational Linguistics
Abbreviated titleACL 2020
CountryUnited States
CityVirtual conference
Internet address

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