Density-based graph model summarization: Attaining better performance and efficiency

• Tipo: Artigo em Revista Científica Internacional
• Data: 2015

Autores

Valizadeh, M (Autor) Outra; Pavel Brazdil (Autor) FEP

Revista

Título: Intelligent Data Analysis

Vol. 19

Páginas: 617-629

ISSN: 1088-467X

Editora: IOS PRESS

Indexação

ISI Web of Knowledge - 1 Citação

Scopus - 1 Citação

Outras Informações

ID Authenticus: P-00G-BSZ

DOI: 10.3233/ida-150735

Abstract (EN): Several algorithms based on PageRank algorithm have been proposed to rank the document sentences in the multi-document summarization field and LexRank and T-LexRank algorithms are well known examples. In literature different concepts such as weighted inter-cluster edge, cluster-sensitive graph model and document-sensitive graph model have been proposed to improve LexRank and T-LexRank algorithms (e.g. DsR-G, DsR-Q) for multi-document summarization. In this paper, a density-based graph model for multi-document summarization is proposed by adding the concept of density to LexRank and T-LexRank algorithms. The resulting generic multi-document summarization systems, DensGS and DensGSD were evaluated on DUC 2004 while the query-based variants, DensQS, DensQSD were evaluated on DUC 2006, DUC 2007 and TAC 2010 task A. ROUGE measure was used in the evaluation. Experimental results show that density concept improves LexRank and T-LexRank algorithms and outperforms previous graph-based models (DsR-G and DsR-Q) in generic and query-based multi-document summarization tasks. Furthermore, the comparison of the number of iterations indicates that the density-based algorithm is faster than the other algorithms based on PageRank.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 13