Please use this identifier to cite or link to this item: http://hdl.handle.net/10625/49746
Title: New Approach for Multi-Document Update Summarization
Authors: Long, Chong
Huang, Min-Lie
Zhu, Xiao-Yan
Li, Ming
Keywords: DATA MINING
TEXT MINING
KOLMOGOROV COMPLEXITY
INFORMATION DISTANCE
DOCUMENT SUMMARIZATION
INFORMATION DISTANCE
CLUSTER ANALYSIS
INFORMATION THEORY
Date: 2010
Citation: Long, C., Huang, M., Zhu, X., & Li, M. (2010). A New Approach for Multi-Document Update Summarization. Journal of Computer Science and Technology, 25 (4): 739-749. doi: 10.1007/s11390-010-9361-x
Abstract: Fast changing knowledge on the Internet can be acquired more efficiently with the help of automatic document summarization and updating techniques. This paper describes a novel approach for multi-document update summarization. The best summary is defined to be the one which has the minimum information distance to the entire document set. The best update summary has the minimum conditional information distance to a document cluster given that a prior document cluster has already been read. Experiments on the DUC/TAC 2007 to 2009 datasets (http://duc.nist.gov/, http://www.nist.gov/tac/) have proved that our method closely correlates with the human summaries and outperforms other programs such as LexRank in many categories under the ROUGE evaluation criterion.
ISSN: 1000-9000
URI: http://hdl.handle.net/10625/49746
Project Number: 104519
Project Title: International Research Chairs Initiative (IRCI)
Access Restriction: Due to copyright restrictions the full text of this research output is not available in the IDRC Digital Library or by request from the IDRC Library. / Compte tenu des restrictions relatives au droit d`auteur, le texte intégral de cet extrant de recherche n`est pas accessible dans la Bibliothèque numérique du CRDI, et il n`est pas possible d`en faire la demande à la Bibliothèque du CRDI.
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