Chapter Captor: Text Segmentation in Novels

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, pages 8373–8383, November 16–20, 2020. c⃝2020 Association for Computational Linguistics 8373 Chapter Captor: Text Segmentation in Novels Charuta Pethe, Allen Kim, Steven Skiena Department of Computer Science, Stony Brook University, NY, USA {cpethe,allekim,skiena}@cs.stonybrook.edu Abstract Books are typically segmented into chap- ters 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 segmen- tation 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. Fi- nally, we reveal interesting historical trends in the chapter structure of novels. 1 Introduction Text segmentation (Hearst, 1994; Beeferman et al., 1999) is a fundamental task in natural language processing, which seeks to partition texts into se- quences of coherent segments or episodes. Segmen- tation tasks differ widely in scale, from partitioning sentences into clauses to dividing large texts into coherent parts, where each segment is ideally an independent event occurring in the narrative. Text segmentation plays an important role in many NLP applications including summarization, information retrieval, and question answering. In the context of literary works, event detection is a central concern in discourse analysis (Joty et al., 2019). In order to obtain representations of events, it is essential to identify narrative boundaries in the text, where one event ends and another begins. In novels and related literary works, authors of- ten define such coherent segments by means of sections and chapters. Chapter boundaries are typ- ically denoted by formatting conventions such as page breaks, white-space, chapter numbers, and titles. This physical segmentation improves the readability of long texts for human readers, provid- ing transition cues for breaks in the story. In this paper, we investigate the task of identi- fying chapter boundaries in literary works, as a proxy for that of large-scale text segmentation. The text of thousands of scanned books are available in repositories such as Project Gutenberg (Gutenberg, n.d.), making the chapter boundaries of these texts an attractive source of annotations to study text segmentation. Unfortunately, the physical manifes- tations of the printed book have been lost in the Gutenberg texts, limiting their usefulness for such studies. Chapter titles and numbers are retained in the texts but not systematically annotated: indeed they sit as hidden obstacles for most NLP analysis of these texts. We develop methods for extracting ground truth chapter segmentation from Gutenberg texts, and use this as training/evaluation data to build text segmentation systems to predict the natural bound- aries of long narratives. Our primary contributions 1 include: • Project Gutenberg Chapter Segmentation Resource: To create a ground-truth …

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