一种基于局部—全局主题关系的演化式摘要系统

带有时间标志的演化式摘要是近年来提出的自然语言处理任务,其本质是多文档自动文摘,它的研究对象是互联网上连续报道的热点新闻文档。针对互联网新闻事件报道的动态演化、动态关联和信息重复等特点,该文提出了一种基于局部—全局主题关系的演化式摘要方法,该方法将新闻事件划分为多个不同的子主题,在考虑时间演化的基础上同时考虑子主题之间的主题演化,最后将新闻标题作为摘要输出。实验结果表明,该方法是有效的,并且在以新闻标题作为输入输出时,和当前主流的多文档摘要和演化摘要方法相比,在Rouge评价指标上有显著提高。

An Evolutionary Summarization System Based on Local-global Topic Relationship

Evolutionary timeline summarization (ETS) for Internet News Event is a new task in natural language processing,which is a kind of multi-document summarization (MDS) in essence. According to the features of dynamic evolution,content relevance and information redundancy of Internet news event,this paper puts forward an evolutionary summarization method basing on local and global topic relations. First,the news event is divided into a number of different sub-topics. In the meantime,the basis of time evolution and the topic evolution between sub-topics are considered. Finally,headlines are extracted as summary. The experimental results show that this method is effective. Especially using news headlines as inputs or outputs brings significant improvements in the Rouge evaluation,compared with current popular method of multi-document summarization and evolution summarization.