Movie Plot Analysis via Turning Point Identification

Pinelopi Papalampidi, Frank Keller, Mirella Lapata

Abstract
According to screenwriting theory, turning points (e.g., change of plans, major setback, climax) are crucial narrative moments within a screenplay: they define the plot structure, determine its progression and segment the screenplay into thematic units (e.g., setup, complications, aftermath). We propose the task of turning point identification in movies as a means of analyzing their narrative structure. We argue that turning points and the segmentation they provide can facilitate processing long, complex narratives, such as screenplays, for summarization and question answering. We introduce a dataset consisting of screenplays and plot synopses annotated with turning points and present an end-to-end neural network model that identifies turning points in plot synopses and projects them onto scenes in screenplays. Our model outperforms strong baselines based on state-of-the-art sentence representations and the expected position of turning points.
Anthology ID:
D19-1180
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Editors:
Kentaro Inui, Jing Jiang, Vincent Ng, Xiaojun Wan
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1707–1717
Language:
URL:
https://aclanthology.org/D19-1180
DOI:
10.18653/v1/D19-1180
Bibkey:
Cite (ACL):
Pinelopi Papalampidi, Frank Keller, and Mirella Lapata. 2019. Movie Plot Analysis via Turning Point Identification. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 1707–1717, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Movie Plot Analysis via Turning Point Identification (Papalampidi et al., EMNLP-IJCNLP 2019)
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PDF:
https://preview.aclanthology.org/ml4al-ingestion/D19-1180.pdf
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 D19-1180.Attachment.zip
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