@inproceedings{ecd56a12923449e58f8c20df668b00fa,
title = "Mining topical relevant patterns for multi-document summarization",
abstract = "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.",
keywords = "Multi-document summarization, Pattern mining, Topic model",
author = "Yutong Wu and Yang Gao and Yuefeng Li and Yue Xu and Meihua Chen",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 2015 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology Workshops, WI-IAT Workshops 2015 ; Conference date: 06-12-2015 Through 09-12-2015",
year = "2016",
month = feb,
day = "2",
doi = "10.1109/WI-IAT.2015.136",
language = "English",
series = "Proceedings - 2015 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "114--117",
booktitle = "Proceedings - 2015 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2015",
address = "United States",
}