# Chapter Captor: Text Segmentation in Novels

arXiv: https://arxiv.org/abs/2011.04163 · HTML: https://mlsanity.com/paper/2011.04163/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Nov 2020.

## Built on by (most cited first, 4 of 4)

- [Topical Segmentation of Spoken Narratives: A Test Case on Holocaust Survivor Testimonies](https://mlsanity.com/paper/2210.13783/) (2022)
- [Prosody Analysis of Audiobooks](https://mlsanity.com/paper/2310.06930/) (2023)
- [The Announcement Carries the Cue: Markup, Boundaries, and the Notation of Pre-Training Corpora](https://mlsanity.com/paper/2608.09093/) (2026)
- [STONYBOOK: A System and Resource for Large-Scale Analysis of Novels](https://mlsanity.com/paper/2311.03614/) (2023)

## Abstract (arXiv)

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.