DocumentCode :
2208019
Title :
Multi-document Summarization Using Minimum Distortion
Author :
Ma, Tengfei ; Wan, Xiaojun
Author_Institution :
MOE Key Lab. of Comput. Linguistics, Peking Univ., Beijing, China
fYear :
2010
fDate :
13-17 Dec. 2010
Firstpage :
354
Lastpage :
363
Abstract :
Document summarization plays an important role in the area of natural language processing and text mining. This paper proposes several novel information-theoretic models for multi-document summarization. They consider document summarization as a transmission system and assume that the best summary should have the minimum distortion. By defining a proper distortion measure and a new representation method, the combination of the last two models (the linear representation model and the facility location model) gains good experimental results on the DUC2002 and DUC2004 datasets. Moreover, we also indicate that the model has high interpretability and extensibility.
Keywords :
data mining; knowledge representation; natural language processing; text analysis; document summarization; information-theoretic model; natural language processing; text mining; J-S Divergence; information-theoretic summarization; linear representation; minimum distortion; multi-document summarization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Data Mining (ICDM), 2010 IEEE 10th International Conference on
Conference_Location :
Sydney, NSW
ISSN :
1550-4786
Print_ISBN :
978-1-4244-9131-5
Electronic_ISBN :
1550-4786
Type :
conf
DOI :
10.1109/ICDM.2010.106
Filename :
5693989
Link To Document :
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