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Chapter Captor: Text Segmentation in Novels

2020-11-09 02:56:54
Charuta Pethe, Allen Kim, Steven Skiena
arXiv_CL
         
Abstract

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.

Abstract (translated)

URL

https://arxiv.org/abs/2011.04163

PDF

https://arxiv.org/pdf/2011.04163.pdf

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