论文标题
章节绑架者：小说中的文本细分
Chapter Captor: Text Segmentation in Novels
论文作者
论文摘要
书籍通常分为章节和部分，代表连贯的亚措施和主题。我们调查了预测章节边界的任务，作为对长期细分的一般任务的代理。我们使用混合方法结合了神经推理和规则匹配，以识别书籍中的章节标题，在此任务上达到了0.77的F1次数，我们建立了一个9,126本英语小说的Gutenberg分会细分数据集。在删除结构提示后，使用这些注释的数据作为基础真理，我们提出了基于剪切的神经方法，用于章节分割，以0.453的F1得分达到0.453，这是对账面长度文档的精确中断预测的具有挑战性的任务。最后，我们揭示了小说一章结构中有趣的历史趋势。
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.
