基于事件项语义图聚类的多文档摘要方法  

Multi-Document Summarization Based on Event Term Semantic Relation Graph Clustering

作　　者：刘茂福[1] 李文捷[2] 姬东鸿[3] 

机构地区：[1]武汉科技大学计算机科学与技术学院,湖北武汉430065 [2]香港理工大学计算机系 [3]武汉大学计算机学院,湖北武汉430072

出　　处：《中文信息学报》2010年第5期77-84,共8页Journal of Chinese Information Processing

基　　金：湖北省自然科学基金资助项目(2009CDB311);国家自然科学基金重大研究计划资助项目(90820005)

摘　　要：基于事件的抽取式摘要方法一般首先抽取那些描述重要事件的句子,然后把它们重组并生成摘要。该文将事件定义为事件项以及与其关联的命名实体,并聚焦从外部语义资源获取的事件项语义关系。首先基于事件项语义关系创建事件项语义关系图并使用改进的DBSCAN算法对事件项进行聚类,接着为每类选择一个代表事件项或者选择一类事件项来表示文档集的主题,最后从文档抽取那些包含代表项并且最重要的句子生成摘要。该文的实验结果证明在多文档自动摘要中考虑事件项语义关系是必要的和可行的。Event-based extractive summarization attempts to extract sentences and re-organize them in a summary according to the important events that the sentences describe. In this paper, we define the event as event terms and their associated entities and emphasize on the event term semantic relations derived from external linguistic resource. Firstly, the graph based on the event term semantic relations is constructed and the event terms in the graph are grouped into clusters using the revised DBSCAN clustering algorithm. Then, we select one event term as the repre- sentative term for each cluster or one cluster to present the main topic of the documents. Lastly, we generate the summary by extracting the sentences which contain more informative representative terms from the documents. The evaluation on the DUC 2001 document sets shows it is necessary to take the semantic relations among the event terms into consideration and our summarization approach based on event term semantic relation graph clustering is effective.

关 键 词：基于事件的摘要 事件语义关系图 DBSCAN聚类算法 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]