  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.
