一种新的ILP词汇多样性内容摘要框架
A Novel ILP Framework for Summarizing Content with High Lexical Variety
摘要：
总结个人贡献的内容可能具有挑战性，因为即使在描述相同的事件时，人们也会做出不同的词汇选择。然而，仍然非常需要总结这些内容。例子包括学生对课后反思性问题的回答，产品评论，以及不同新闻机构发表的与同一事件相关的新闻文章。这些文档的高度词汇多样性阻碍了系统有效识别突出内容和减少摘要冗余的能力。在本文中，我们克服了这个问题，通过引入一个整数线性规划为基础的摘要框架。它采用了一个低秩近似的词汇-词共现矩阵，本质上组语义相似的词汇项目。我们对学生反应，产品评论和新闻文档的数据集进行了广泛的实验。我们的方法比较有利的一些提取基线，以及神经抽象的总结系统。本文最后阐明了何时以及为什么所提出的框架是有效的，在总结内容与高词汇多样性。
英文摘要：
Summarizing content contributed by individuals can be challenging, because people make different lexical choices even when describing the same events. However, there remains a significant need to summarize such content. Examples include the student responses to post-class reflective questions, product reviews, and news articles published by different news agencies related to the same events. High lexical diversity of these documents hinders the system's ability to effectively identify salient content and reduce summary redundancy. In this paper, we overcome this issue by introducing an integer linear programming-based summarization framework. It incorporates a low-rank approximation to the sentence-word co-occurrence matrix to intrinsically group semantically-similar lexical items. We conduct extensive experiments on datasets of student responses, product reviews, and news documents. Our approach compares favorably to a number of extractive baselines as well as a neural abstractive summarization system. The paper finally sheds light on when and why the proposed framework is effective at summarizing content with high lexical variety.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
