DocumentCode :
3318278
Title :
A cue-based hub-authority approach for multi-document text summarization
Author :
Zhang, Junlin ; Sun, Le ; Zhou, Quan
Author_Institution :
Open Syst. & Chinese Inf. Process. Center, Chinese Acad. of Sci., Beijing, China
fYear :
2005
fDate :
30 Oct.-1 Nov. 2005
Firstpage :
642
Lastpage :
645
Abstract :
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.
Keywords :
graph theory; text analysis; HITS; PageRank; cue-based hub-authority approach; graph-based sentence ranking algorithms; multidocument extractive summarization; multidocument generic text summarization; sentence centrality concept; Data mining; Information processing; Internet; Open systems; Supervised learning; System testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Natural Language Processing and Knowledge Engineering, 2005. IEEE NLP-KE '05. Proceedings of 2005 IEEE International Conference on
Print_ISBN :
0-7803-9361-9
Type :
conf
DOI :
10.1109/NLPKE.2005.1598815
Filename :
1598815
Link To Document :
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