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A cue-based hub-authority approach for multi-document text summarization

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3 Author(s)
Junlin Zhang ; Open Syst. & Chinese Inf. Process. Center, Chinese Acad. of Sci., Beijing, China ; Le Sun ; Quan Zhou

Multi-document extractive summarization relies on the concept of sentence centrality to identify the most important sentences in a document. Although some research has introduced the graph-based ranking algorithms such as PageRank and HITS into the text summarization, we propose a new approach under the hub-authority framework in this paper. Our approach combines the text content with some cues such as "cue phrase", "sentence length" and "first sentence" and explores the sub-topics in the multi-documents by bringing the features of these sub-topics into graph-based sentence ranking algorithms. We provide an evaluation of our method on DUC 2004 data. The results show that our approach is an effective graph-ranking schema in multi-document generic text summarization.

Published in:

Natural Language Processing and Knowledge Engineering, 2005. IEEE NLP-KE '05. Proceedings of 2005 IEEE International Conference on

Date of Conference:

30 Oct.-1 Nov. 2005

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