# Paragraph Boundary Recognition in Novels for Story Understanding
## 1 Introduction  
With the improvement in natural-language processing, commercial expectations for the automatic generation of scenarios in novels, movies, games, etc. have been increasing. For example, in Japan, there is a literary award that explicitly allows for works written by artificial intelligence ([[https://hoshiaward.nikkei.co.jp/](https://hoshiaward.nikkei.co.jp/)]([https://hoshiaward.nikkei.co.jp/](https://hoshiaward.nikkei.co.jp/)), accessed on 18 June 2021). Studies on story generation have been proposed since ancient times [1; 2; 3]; recently, various approaches based on neural networks were proposed [4; 5; 6; 7; 8]. However, it is very difficult to generate high-quality narrative sentences that satisfy readers. Therefore, it is important for the computer to understand the special sentence structure of the novel as a step-by-step study with the ultimate goal of the automatic generation of the novel by the computer. A computer understanding a story such as a novel is generally a task of modeling the consistency of sentences. Thus far, research has been conducted on the understanding and generation of narrative sentences from various perspectives such as the Story Cloze Test (SCT) [9; 10] and Story Completion (SC) [11], ordering sentences into a story [12; 13; 14]. In relation to tasks aimed at story understanding by a computer as described above, we focused on segmenting text from novels into paragraphs, which is an important technique used in sentence writing in novels. It is also an operation that divides a document into paragraphs to improve readability. [PERSON] et al. [15] discussed the importance of paragraphs in texts. In their study, the effect of a paragraph display as a document layout on the reader's understanding of content was investigated. Specifically, they conducted an experiment in which a collaborator read correctly paragraphed sentences, intentionally incorrectly paragraphed sentences, and unparagraphedsentences, and then comparedtheir comprehension of the contents. They concluded that a proper paragraph setting was important for facilitating the reader's understanding of the document. The features of paragraphs are slightly different between descriptive or logical sentences, such as academic treatises, and literary sentences, such as in novels and essays. This is essential because logical texts focus on accurately and concisely disclosing information to the reader, whereas literary texts focus on emotionally impressing on the reader [16]. In the former, one paragraph focuses solely on one topic, centered on a core sentence, which is called a topic sentence. It is necessary for the content and assertions in each sentence composing a paragraph to be consistent, and studies numerically defined the consistency of paragraphs for scientific and technological sentences [17]. By contrast, the latter is based on the transition of topics and scenes over time, and generally does not apply a topic sentence [16]. Therefore, there are no common rules that the writer should uniformly follow in the text segmentation of a novel, and such segmentation is an extremely difficult operation that requires a high level of skill. Although the demand for systems that assist in text segmentation of novels into paragraphs is high, few studies have been conducted in this area owing to the difficulty. Placing new paragraphs in the appropriate positions on the basis of the transitions of scenes and topics helps readers to fully understand the story. Therefore, the position where a new paragraph starts contains important sensory information for people who write or read novels. On the basis of this assumption, in this study, as a stepwise approach with the automatic creation of a novel as the ultimate objective, we estimate paragraph boundaries from the perspective of a computer's understanding of the story in the novel. We regarded this task as a binary classification problem regarding whether target sentences belong to the same paragraph, which we call \"paragraph-boundary recognition\".