Chapter Captor: Text Segmentation in Novels
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.
Keywords
Cite
@article{arxiv.2011.04163,
title = {Chapter Captor: Text Segmentation in Novels},
author = {Charuta Pethe and Allen Kim and Steven Skiena},
journal= {arXiv preprint arXiv:2011.04163},
year = {2020}
}
Comments
11 pages, 10 figures, Accepted at EMNLP 2020 as a long paper