Applying wikipedia-based explicit semantic analysis for query-biased document summarization

Authors:
Yunqing Zhou;Zhongqi Guo;Peng Ren;Yong Yu
Affiliations:
Dept. of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China;Dept. of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China;Dept. of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China;Dept. of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
Venue:
ICIC'10 Proceedings of the 6th international conference on Advanced intelligent computing theories and applications: intelligent computing
Year:
2010

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Abstract

Query-biased summary is a query-centered document brief representation. In many scenarios, query-biased summarization can be accomplished by implementing query-customized ranking of sentences within the web page. However, it is a tough work to generate this summary since it is hard to consider the similarity between the query and the sentences of a particular document for lacking of information and background knowledge behind these short texts. We focused on this problem and improved the summary generation effectiveness by involving semantic information in the machine learning process. And we found these improvements are more significant when query term occurrences are relatively low in the document.