TY - GEN
T1 - Automatic multi-document summarization based on new sentence similarity measures
AU - Yin, Wenpeng
AU - Pei, Yulong
AU - Huang, Lian'en
N1 - Funding Information:
Abdulrahman Almuhareb is supported by King Abdu-laziz City for Science and Technology (KACST), Riyadh, Saudi Arabia.
Funding Information:
Abdulrahman Almuhareb is supported by King Abdulaziz City for Science and Technology (KACST), Riyadh, Saudi Arabia.
PY - 2012
Y1 - 2012
N2 - The acquiring of sentence similarity has become a crucial step in graph-based multi-document summarization algorithms which have been intensively studied during the past decade. Previous algorithms generally considered sentence-level structure information and semantic similarity separately, which, consequently, had no access to grab similarity information comprehensively. In this paper, we present a general framework to exemplify how to combine the two factors above together so as to derive a corpus-oriented and more discriminative sentence similarity. Experimental results on the DUC2004 dataset demonstrate that our approaches could improve the multi-document summarization performance to a considerable extent.
AB - The acquiring of sentence similarity has become a crucial step in graph-based multi-document summarization algorithms which have been intensively studied during the past decade. Previous algorithms generally considered sentence-level structure information and semantic similarity separately, which, consequently, had no access to grab similarity information comprehensively. In this paper, we present a general framework to exemplify how to combine the two factors above together so as to derive a corpus-oriented and more discriminative sentence similarity. Experimental results on the DUC2004 dataset demonstrate that our approaches could improve the multi-document summarization performance to a considerable extent.
UR - https://www.scopus.com/pages/publications/84867686879
UR - https://www.scopus.com/pages/publications/84867686879#tab=citedBy
U2 - 10.1007/978-3-642-32695-0_81
DO - 10.1007/978-3-642-32695-0_81
M3 - Conference contribution
AN - SCOPUS:84867686879
SN - 9783642326943
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 832
EP - 837
BT - PRICAI 2012
T2 - 12th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2012
Y2 - 3 September 2012 through 7 September 2012
ER -