This item is a Poster.
- Zhu, Junyan - Zhejiang University of Finance and Economics
- Wang, Can - Zhejiang University of Finance and Economics
- He, Xiaofei - Zhejiang University of Finance and Economics
- Bu, Jiajun - Zhejiang University of Finance and Economics
- Chen, Chun - Zhejiang University of Finance and Economics
- Shang, Shujie - Zhejiang University of Finance and Economics
- Qu, Mingcheng - Zhejiang University of Finance and Economics
- Lu, Gang - Zhejiang University of Finance and Economics
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Abstract
Social annotations on a Web document are highly generalized description of topics contained in that page. Their tagged frequency indicates the user attentions with various degrees. This makes annotations a good resource for summarizing multiple topics in a Web page. In this paper, we present a tag-oriented Web document summarization approach by using both document content and the tags annotated on that document. To improve summarization performance, a new tag ranking algorithm named EigenTag is proposed in this paper to reduce noise in tags. Meanwhile, association mining technique is employed to expand tag set to tackle the sparsity problem. Experimental results show our tag-oriented summarization has a significant improvement over those not using tags.
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