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Volumn 6, Issue 5, 2011, Pages 298-304

Combined features to maximal marginal relevance algorithm for multi-document summarization

Author keywords

Maximal marginal relevance algorithm; Sentence similarity; Summarization

Indexed keywords

COMBINED FEATURES; MAXIMAL MARGINAL RELEVANCE ALGORITHM; MULTI-DOCUMENT SUMMARIZATION; RELEVANT DOCUMENTS; SENTENCE SIMILARITY; SUMMARIZATION; SVM ALGORITHM;

EID: 79958063727     PISSN: 19759320     EISSN: None     Source Type: Journal    
DOI: 10.4156/jcit.vol6.issue5.34     Document Type: Article
Times cited : (7)

References (15)
  • 8
    • 78651563590 scopus 로고    scopus 로고
    • People Summarization by combining named entity recognition and relation extraction
    • Xiaojiang Liu, Nenghai Yu, "People Summarization by Combining Named Entity Recognition and Relation Extraction", JCIT: Journal of Convergence Information Technology, Vol. 5, No. 10, pp. 233-241, 2010.
    • (2010) JCIT: Journal of Convergence Information Technology , vol.5 , Issue.10 , pp. 233-241
    • Liu, X.1    Nenghai, Y.2
  • 14
    • 0032270694 scopus 로고    scopus 로고
    • The Use of MMR, diversity-based reranking for reordering documents and producing summaries
    • J. Carbonell, J. Goldstein, "The Use of MMR, Diversity-Based Reranking for Reordering Documents and Producing Summaries", In Proceeding of SIGIR98, pp. 167-173,1998.
    • (1998) In Proceeding of SIGIR98 , pp. 167-173
    • Carbonell, J.1    Goldstein, J.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.