Subtopic-based multi-document summarization

This paper proposes a novel approach for multi-document summarization based on subtopic segmentation. It firstly detects the subtopics in a topic, and then finds the central sentence for each subtopic. The sentences are scored based on their importance in the document and in the subtopic. Two anti-r...

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Bibliographic Details
Published in2009 International Conference on Machine Learning and Cybernetics Vol. 6; pp. 3505 - 3510
Main Authors Lin Dai, Ji-Liang Tang, Yun-Qing Xia
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2009
Subjects
Anti-redundancy strategy
Computer science
Cybernetics
Data mining
Information science
Information technology
Laboratories
Learning systems
Machine learning
Multi-document summarization
Natural language processing
Partial response channels
Topic Detection and Tracking
Topic segmentation
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Summary:This paper proposes a novel approach for multi-document summarization based on subtopic segmentation. It firstly detects the subtopics in a topic, and then finds the central sentence for each subtopic. The sentences are scored based on their importance in the document and in the subtopic. Two anti-redundancy strategies are used to extract sentences to form summarization. Since our approach is intrinsically incremental, it is effective when new documents are added to the document set. Experimental results indicate that the proposed approach is effective and efficient.
ISBN:9781424437023
1424437024
ISSN:2160-133X
DOI:10.1109/ICMLC.2009.5212767