基于事件抽取的网络新闻多文档自动摘要

目前,有代表性的自动摘要方法是根据文本片段进行聚类,较传统方法避免了信息冗余,但网络新闻文本中有些文本片段和主题无关,影响了聚类的效果,导致最终生成的摘要不够简洁。为此,该文引入事件抽取技术,提出了一种基于事件抽取的网络新闻多文档自动摘要方法。该方法首先通过二元分类器辨析出文本中的事件和非事件;然后通过聚类将文档原来以段落或句子为单位的物理划分转化为以事件为单位的内容逻辑划分,最后通过主旨事件抽取、排序及润色,生成摘要。实验结果表明,该方法是有效的,显著提高了生成摘要的质量。

Web News Multi-document Summarization Based on Event Extraction

State-of-the-art automatic summarization is based on text segment clustering to avoid redundancy defects in the traditional approaches.But some of the text segments in the web news are irrelevant to the subject,which affects the result of clustering and damages the conciseness of summarization.This paper introduces the event extraction technology and proposes an event extraction based web news multi-document summarization method.Firstly,the method distinguishes event and non-event from the news through a binary classifier.Then,the original documents’ physical division based on paragraphs or sentences are transformed into event based content logical division through clustering.Finally,the summarization is derived from the extraction,taxis and embellishment of the major events.Experimental results demonstrate the effectiveness of the proposed method,which improves summarization quality significantly.