论文标题
探索内容选择在新颖章节的总结中
Exploring Content Selection in Summarization of Novel Chapters
论文作者
论文摘要
我们提出了一项新的摘要任务，使用在线学习指南中的摘要/分会对生成了新章节的摘要。鉴于章节长度以及摘要中的极端释义和概括，这比新闻摘要任务更难。我们专注于提取性总结，这需要创建一组金标准的提取摘要。我们提出了一个新的度量标准，用于将参考摘要句与章节句子对齐，以创建黄金提取物，并尝试使用不同的对齐方法进行实验。我们的实验表明，通过自动指标和众包金字塔分析所示，对我们任务的先前对准方法有了显着改善。我们在https://github.com/manestay/novel-chapter-dataset上提供数据收集脚本。
We present a new summarization task, generating summaries of novel chapters using summary/chapter pairs from online study guides. This is a harder task than the news summarization task, given the chapter length as well as the extreme paraphrasing and generalization found in the summaries. We focus on extractive summarization, which requires the creation of a gold-standard set of extractive summaries. We present a new metric for aligning reference summary sentences with chapter sentences to create gold extracts and also experiment with different alignment methods. Our experiments demonstrate significant improvement over prior alignment approaches for our task as shown through automatic metrics and a crowd-sourced pyramid analysis. We make our data collection scripts available at https://github.com/manestay/novel-chapter-dataset .
