Multilingual Multi-document Summarization with Enhanced hLDA Features

摘要：

　　This paper presents the state of art research progress on multilingual multi-document summarization.Our method utilizes hLDA(hierarchical Latent Dirichlet Allocation)algorithm to model the documents firstly.A new feature is proposed from the hLDA modeling results,which can reflect semantic information to some extent.Then it combines this new feature with different other features to perform sentence scoring.According to the results of sentence score,it extracts candidate summary sentences from the documents to generate a summary.We have also attempted to verify the effectiveness and robustness of the new feature through experiments.After the comparison with other summarization methods,our method reveals better performance in some respects.

关键词： multilingual multi-document summarization sentence scoring hLDA modeling

作者: Taiwen Huang Lei Li Yazhao Zhang

作者单位: Beijing University of Posts and Telecommunications,Beijing 100876,China

会议类型: 国内会议

会议名称: 第十五届全国计算语言学学术会议(CCL2016)暨第四届基于自然标注大数据的自然语言处理国际学术研讨会(NLP-NABD-2016)

会议地点: 烟台

会议语种:英文

页码: 1-12

在线出版日期: 2016-10-14（万方平台首次上网日期，不代表论文的发表时间）