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ABSTRACT
The problem of using topic representations for multi-document summarization (MDS) has received considerable attention recently. In this paper, we describe five different topic representations and introduce a novel representation of topics based on topic themes. We present eight different methods of generating MDS and evaluate each of these methods on a large set of topics used in past DUC workshops. Our evaluation results show a significant improvement in the quality of summaries based on topic themes over MDS methods that use other alternative topic representations.
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Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.
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CITED BY 14
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Xiaohua Zhou , Xiaohua Hu , Xiaodan Zhang , Xia Lin , Il-Yeol Song, Context-sensitive semantic smoothing for the language modeling approach to genomic IR, Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, August 06-11, 2006, Seattle, Washington, USA
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Jin Zhang , Xueqi Cheng , Gaowei Wu , Hongbo Xu, AdaSum: an adaptive model for summarization, Proceeding of the 17th ACM conference on Information and knowledge management, October 26-30, 2008, Napa Valley, California, USA
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Liangda Li , Ke Zhou , Gui-Rong Xue , Hongyuan Zha , Yong Yu, Enhancing diversity, coverage and balance for summarization through structure learning, Proceedings of the 18th international conference on World wide web, April 20-24, 2009, Madrid, Spain
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