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        <title>Chapter Captor: Text Segmentation in Novels</title>
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        <namePart type="given">Charuta</namePart>
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        <namePart type="family">Kim</namePart>
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        <namePart type="given">Steve</namePart>
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            <title>Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)</title>
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        <name type="personal">
            <namePart type="given">Bonnie</namePart>
            <namePart type="family">Webber</namePart>
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        <name type="personal">
            <namePart type="given">Trevor</namePart>
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            <namePart type="given">Yulan</namePart>
            <namePart type="family">He</namePart>
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            <namePart type="given">Yang</namePart>
            <namePart type="family">Liu</namePart>
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    <abstract>Books are typically segmented into chapters and sections, representing coherent sub-narratives and topics. We investigate the task of predicting chapter boundaries, as a proxy for the general task of segmenting long texts. We build a Project Gutenberg chapter segmentation data set of 9,126 English novels, using a hybrid approach combining neural inference and rule matching to recognize chapter title headers in books, achieving an F1-score of 0.77 on this task. Using this annotated data as ground truth after removing structural cues, we present cut-based and neural methods for chapter segmentation, achieving a F1-score of 0.453 on the challenging task of exact break prediction over book-length documents. Finally, we reveal interesting historical trends in the chapter structure of novels.</abstract>
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%0 Conference Proceedings
%T Chapter Captor: Text Segmentation in Novels
%A Pethe, Charuta
%A Kim, Allen
%A Skiena, Steve
%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 pethe-etal-2020-chapter
%X Books are typically segmented into chapters and sections, representing coherent sub-narratives and topics. We investigate the task of predicting chapter boundaries, as a proxy for the general task of segmenting long texts. We build a Project Gutenberg chapter segmentation data set of 9,126 English novels, using a hybrid approach combining neural inference and rule matching to recognize chapter title headers in books, achieving an F1-score of 0.77 on this task. Using this annotated data as ground truth after removing structural cues, we present cut-based and neural methods for chapter segmentation, achieving a F1-score of 0.453 on the challenging task of exact break prediction over book-length documents. Finally, we reveal interesting historical trends in the chapter structure of novels.
%R 10.18653/v1/2020.emnlp-main.672
%U https://aclanthology.org/2020.emnlp-main.672
%U https://doi.org/10.18653/v1/2020.emnlp-main.672
%P 8373-8383

Markdown (Informal)

[Chapter Captor: Text Segmentation in Novels](https://aclanthology.org/2020.emnlp-main.672) (Pethe et al., EMNLP 2020)

• Chapter Captor: Text Segmentation in Novels (Pethe et al., EMNLP 2020)

ACL

• Charuta Pethe, Allen Kim, and Steve Skiena. 2020. Chapter Captor: Text Segmentation in Novels. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 8373–8383, Online. Association for Computational Linguistics.