C04-1108 experiment of sentence ordering through multi-document summarization . The proposed method which utilizes
C04-1108 experiment of sentence ordering through multi-document summarization to test the effectiveness of
C04-1093 summarization method . Our method is multi-document summarization ( MDS ) ( Mani , 2001 ) . Because
D08-1079 the topic cluster . Automated multi-document summarization has drawn much attention in recent
C02-1085 Fukumoto Yoshimi Suzuki Abstract For multi-document summarization where documents are collected
C04-1129 improving content selection in multi-document summarization . In particular , we show how
C04-1108 to sentence ordering in case of multi-document summarization . Sentence position in the original
D08-1079 influence the summary quality in multi-document summarization . Graph-based models have been
D08-1079 proposed model . 1 Introduction Multi-document summarization aims to produce a summary describing
D08-1032 was very successful in generic multi-document summarization . A topic-sensitive LexRank is
C04-1094 interested in its application to multi-document summarization , both for the automatic generation
D08-1079 has been recently exploited for multi-document summarization by making only use of the sentence-to-sentence
C04-1108 , which consists of 30 sets of multi-document summarization tasks . For more information
C04-1094 generated by human subjects after a multi-document summarization process . 3 Test bed : the ISCORPUS
C04-1094 nine subjects performed a complex multi-document summarization task for eight different topics
D08-1028 response text and then applying multi-document summarization techniques to collate the representative
C04-1108 sentence ordering in the context of multi-document summarization and the impact of sentence ordering
C04-1093 proposed in this paper is related to multi-document summarization ( MDS ) ( Mani , 2001 ; Radev
C04-1108 summarization , is not enough for multi-document summarization because we must consider inter-document
D08-1079 been successfully applied for multi-document summarization by making use of the " voting
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