Web-Page Summarization Using Clickthrough Data
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Description: By Jian-Tao Sun, Dou Shen, HuaJun Zeng, Qiang Yang, Yuchang Lu and Zheng Chen. In: Proceedings of the 28th Annual International ACM SIGIR Conference, August 2005. The authors propose two adapted summarization methods that take advantage of the relationships discovered from clickthrough data. For those pages not covered by clickthrough data, they put forward a thematic lexicon approach to generate implicit knowledge. The methods are evaluated on a relatively small dataset consisting of manually annotated pages as well as a large dataset crawled from ODP.
Web-Page Summarization Using Clickthrough Data - Microsoft Research Web-Page Summarization Using Clickthrough Data Jian-Tao Sun, Dou Shen, Huajun Zeng, Qiang Yang, Yuchang Lu, and Zheng Chen Most previous Web-page summarization methods treat a Web page as plain text. However, such methods fail to un- cover the full knowledge associated with aWeb page to build a high-quality summary, because the Web contains many hidden relationships that are not used in these methods. Uncovering the inherent knowledge is important to building good Web-page summarizers. In this paper, we extract the extra knowledge from the clickthrough data of a Web search engine to improve Web-page summarization. We first ana- lyze the feasibility to utilize clickthrough data in text sum- marization, and then propose two adapted summarization methods that take advantage of the relationships discovered from the clickthrough data. For those pages not covered by the clickthrough data, we put forward a thematic lexi- con approach to generate implicit knowledge for them. Our methods are evaluated on a relatively small dataset consist- ing of manually annotated pages as well as a large dataset that is crawled from the Open Directory Project website. The experimental results indicate that significant improve- ments can be achieved through our proposed summarizer as compared with summarizers without using the clickthrough data.
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