News Summarization Based on Semantic Similarity Measure

摘要：

This paper proposed a new method of news summarization based on semantic similarity measure. It used Latent semantic indexing (LSI) to measure sentence similarity, then it used Singular Value Decomposition (SVD) to reduce the dimension of the word-sentence matrix, it used new clustering algorithm to cluster all the sentences. It ordered all the sentences according to their relevant positions in the original document. Experimental result shows that the proposed method can improve the performance of summary.

关键词： news summarization Latent semantic indexing sentence similarity measure

作者: Hui Yu

作者单位: Institute of Computer & Communication Engineering, China University of Petroleum, Shandong, China, 257061

会议类型: 国际会议

会议名称: 2009 Ninth International Conference on Hybrid Intelligent Systems(第九届混合智能系统国际会议 HIS 2009)

会议地点: 沈阳

会议语种:英文

页码: 1-4

在线出版日期: 2009-08-12（万方平台首次上网日期，不代表论文的发表时间）