Mining topical relevant patterns for multi-document summarization

Wu, Yutong, Gao, Yang, Li, Yuefeng, Xu, Yue, & Chen, Meihua (2015) Mining topical relevant patterns for multi-document summarization. In 2015 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), IEEE, Singapore, pp. 114-117.

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Multi-document summarization addressing the problem of information overload has been widely utilized in the various real-world applications. Most of existing approaches adopt term-based representation for documents which limit the performance of multi-document summarization systems. In this paper, we proposed a novel pattern-based topic model (PBTMSum) for the task of the multi-document summarization. PBTMSum combining pattern mining techniques with LDA topic modelling could generate discriminative and semantic rich representations for topics and documents so that the most representative and non-redundant sentences can be selected to form a succinct and informative summary. Extensive experiments are conducted on the data of document understanding conference (DUC) 2007. The results prove the effectiveness and efficiency of our proposed approach.

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ID Code: 94109
Item Type: Conference Paper
Refereed: Yes
Keywords: multi-document summarization, pattern mining, topic model
DOI: 10.1109/WI-IAT.2015.136
ISBN: 9781467396189
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Natural Language Processing (080107)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Pattern Recognition and Data Mining (080109)
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2015 IEEE
Deposited On: 04 Apr 2016 00:59
Last Modified: 08 Apr 2016 15:20

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