C hapter C aptor: T ext S egmentation in N ovels (2020)

Abstract: Books are typically segmented into chapters 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 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 a 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.

## introduction text segmentation @xcite is a fundamental task in natural language processing, which seeks to partition texts into sequences of coherent segments or episodes. segmentation 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 @xcite . 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 often define such coherent segments by means of sections and chapters. chapter boundaries are typically 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, providing transition cues for breaks in the story. in this paper, we investigate the task of identifying 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 manifestations 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 boundaries of long narratives. our primary contributions foot_0 include: ## previous work many approaches have been developed in recent years to address variants of the task of identifying structural elements in books. @xcite attempt this task at the page-level, by assigning a label (e.g. preface, index, table of contents, etc.) to each page of the book. @xcite address the task of recognizing and extracting tables of contents from book documents, with a focus on identifying its style. participants of the book structure extraction competition at icdar 2013 @xcite attempted to use various approaches for the task. these include making use of the table of contents, ocr information, whitespace, and indentation. @xcite present approaches to identify a table of contents in a book, and @xcite attempt to structure a document according to its table of contents. however, our approach relies only on text, and does not require positional information or ocr coordinates to extract front matter and headings from book texts. for text segmentation, many approaches have been developed over the past years, suitable for different types of data, such as news articles, scientific article, wikipedia pages, and conversation transcripts. the texttiling algorithm (hearst, 1994) makes use of lexical frequency distributions across blocks of a fixed number of words. dotplotting @xcite is a graphical technique to locate discourse boundaries using lexical cohesion across the entire document. yamron et al. (1998) and @xcite propose methods to identify story boundaries in news transcripts. the c99 algorithm @xcite uses a global lexical similarity matrix and a ranking scheme for divisive clustering. @xcite further proposed the use of latent semantic analysis (lsa) to compute inter-sentence similarity. @xcite proposed a statistical model to find the maximum probability segmentation. the minimum cut model @xcite addresses segmentation as a graph partitioning task. this problem has also been addressed in a bayesian setting @xcite . topictiling @xcite is a modification of the texttiling algorithm, and makes use of lda for topic modeling. segmentation using sentence similarity has been extensively explored using affinity propagation @xcite . more recent approaches @xcite involve the use of semantic representations of words to compute sentence similarities. @xcite and @xcite propose neural models to identify break points within the text. @xcite address the slightly different, but relevant task of event prediction using a neural model, on a human-annotated dataset of short events. ## header annotation in order to create a ground-truth dataset for chapter segmentation, we first build a system to recognize chapter headings, using a hybrid approach combining a neural model with a regular expression (regex)-based rule matching system. ## local methods for segmentation after removing all explicit signals of chapter boundaries from the texts, we now evaluate algorithms for segmenting text into chapters. we formulate our task as follows: given: sentences s 0 , s 1 , ..., s n -1 in the book, and p , the number of breaks to insert compute: p break points b 0 , b 1 , ..., b p -1 ∈ {0, 1, ..., n -1} corresponding to chapter breaks. ## global break prediction in the approaches described above, we simply select the highest scoring p points. however, this selection does not conform to spatial constraints. for example, the model may place two breaks close to one another, when realistically, chapter breaks are spaced fairly apart in practice. to validate this, we compute the coefficient of variance (cv) for each book, in terms of the number of sentences per chapter. figure 6 shows the distribution of the cv over all books in our dataset. most books in our dataset have a low cv (distributions with cv less than 1 are considered to be low-variance), reflecting the fact that chapters breaks are spaced fairly equally apart. hence, we propose a dynamic programming approach, in order to incorporate a weight for keeping the chapter breaks equidistant. we formulate the task in the same way as described previously, with an additional parameter α, which determines the importance of the confidence scores as compared to equidistant breaks. α ranges from 0 to 1, where 1 indicates that full weight is given to confidence scores, and 0 indicates that full weight is given to keeping the breaks equidistant. we define the cost of inserting a break at point n and inserting k breaks in points 0 to n -1 recursively as: ## conclusion and future work we build a chapter segmentation dataset resource consisting of 9,126 english fiction novels, using a hybrid approach combining neural inference and regular expression-based rule matching. we achieve and f1 score of 0.77 on this task. further, we use this dataset, remove structural cues, and address the task of predicting chapter boundaries. we present two methods for chapter segmentation. our supervised approach achieves the best performance in exact break prediction, while our unsupervised approach provides information about potential subchapter break points. our work opens up avenues for further research in text segmentation, with potential applications in summarization and discourse analysis. potential future work includes combining the neural and cut-based approaches into a stronger method. finally, it would be interesting to do a deeper dive into variations of author strategies in chapterization, focusing more intently on books with large numbers of short chapters as being more reflective of episode boundaries.