Books are typically segmented into chapters and sections, representing coherent subnarratives 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 an 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.
Chapter Captor: Text Segmentation in Novels
A hybrid neural and rule-based approach was used to create a large dataset for chapter segmentation in English novels, achieving high accuracy, and further models were developed to predict exact chapter breaks in full books.
- Year
- 2020
- Venue
- EMNLP 2020 11
- Authors
- 3
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- Abstract onlyARXIV-DEFAULT
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- arxiv.org/abs/2011.04163ARXIV-DEFAULT
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