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        <title>Multi-view Story Characterization from Movie Plot Synopses and Reviews</title>
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        <namePart type="given">Sudipta</namePart>
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        <namePart type="given">Thamar</namePart>
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            <namePart type="given">Bonnie</namePart>
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        <name type="personal">
            <namePart type="given">Trevor</namePart>
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    <abstract>This paper considers the problem of characterizing stories by inferring properties such as theme and style using written synopses and reviews of movies. We experiment with a multi-label dataset of movie synopses and a tagset representing various attributes of stories (e.g., genre, type of events). Our proposed multi-view model encodes the synopses and reviews using hierarchical attention and shows improvement over methods that only use synopses. Finally, we demonstrate how we can take advantage of such a model to extract a complementary set of story-attributes from reviews without direct supervision. We have made our dataset and source code publicly available at https://ritual.uh.edu/multiview-tag-2020.</abstract>
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%0 Conference Proceedings
%T Multi-view Story Characterization from Movie Plot Synopses and Reviews
%A Kar, Sudipta
%A Aguilar, Gustavo
%A Lapata, Mirella
%A Solorio, Thamar
%Y Webber, Bonnie
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F kar-etal-2020-multi
%X This paper considers the problem of characterizing stories by inferring properties such as theme and style using written synopses and reviews of movies. We experiment with a multi-label dataset of movie synopses and a tagset representing various attributes of stories (e.g., genre, type of events). Our proposed multi-view model encodes the synopses and reviews using hierarchical attention and shows improvement over methods that only use synopses. Finally, we demonstrate how we can take advantage of such a model to extract a complementary set of story-attributes from reviews without direct supervision. We have made our dataset and source code publicly available at https://ritual.uh.edu/multiview-tag-2020.
%R 10.18653/v1/2020.emnlp-main.454
%U https://aclanthology.org/2020.emnlp-main.454
%U https://doi.org/10.18653/v1/2020.emnlp-main.454
%P 5629-5646

Markdown (Informal)

[Multi-view Story Characterization from Movie Plot Synopses and Reviews](https://aclanthology.org/2020.emnlp-main.454) (Kar et al., EMNLP 2020)

• Multi-view Story Characterization from Movie Plot Synopses and Reviews (Kar et al., EMNLP 2020)

ACL

• Sudipta Kar, Gustavo Aguilar, Mirella Lapata, and Thamar Solorio. 2020. Multi-view Story Characterization from Movie Plot Synopses and Reviews. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 5629–5646, Online. Association for Computational Linguistics.