标题:A Comprehensive Method for Text Summarization Based on Latent Semantic Analysis
作者:Wang, Yingjie; Ma, Jun
作者机构:[Wang, Yingjie; Ma, Jun] Shandong Univ, Sch Comp Sci & Technol, Jinan 250100, Peoples R China.
会议名称:2nd Annual Conference of China-Computer-Federation Technical-Committee-of-Chinese-Information on Natural Language Processing and Chinese Computing (CCF TCCI NLPCC)
会议日期:NOV 15-19, 2013
来源:NATURAL LANGUAGE PROCESSING AND CHINESE COMPUTING, NLPCC 2013
出版年:2013
卷:400
页码:394-401
DOI:10.1007/978-3-642-41644-6_38
关键词:Text Summarization; Latent Semantic Analysis; Singular Value; Decomposition
摘要:Text summarization aims at getting the most important content in a condensed form from a given document while retains the semantic information of the text to a large extent. It is considered to be an effective way of tackling information overload. There exist lots of text summarization approaches which are based on Latent Semantic Analysis (LSA). However, none of the previous methods consider the term description of the topic. In this paper, we propose a comprehensive LSA-based text summarization algorithm that combines term description with sentence description for each topic. We also put forward a new way to create the term by sentence matrix. The effectiveness of our method is proved by experimental results. On the summarization performance, our approach obtains higher ROUGE scores than several well known methods.
收录类别:CPCI-S;EI;SCOPUS
WOS核心被引频次:3
Scopus被引频次:7
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84901496742&doi=10.1007%2f978-3-642-41644-6_38&partnerID=40&md5=760df752e751fc2581f416cf84e74611
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