TY - GEN
T1 - A cluster-sensitive graph model for query-oriented multi-document summarization
AU - Wei, Furu
AU - Li, Wenjie
AU - Lu, Qin
AU - He, Yanxiang
PY - 2008/4/14
Y1 - 2008/4/14
N2 - In this paper, we develop a novel cluster-sensitive graph model for query-oriented multi-document summarization. Upon it, an iterative algorithm, namely QoCsR, is built. As there is existence of natural clusters in the graph in the case that a document comprises a collection of sentences, we suggest distinguishing intra- and inter-document sentence relations in order to take into consideration the influence of cluster (i.e. document) global information on local sentence evaluation. In our model, five kinds of relations are involved among the three objects, i.e. document, sentence and query. Three of them are new and normally ignored in previous graph-based models. All these relations are then appropriately formulated in the QoCsR algorithm though in different ways. ROUGE evaluations shows that QoCsR can outperform the best DUC 2005 participating systems.
AB - In this paper, we develop a novel cluster-sensitive graph model for query-oriented multi-document summarization. Upon it, an iterative algorithm, namely QoCsR, is built. As there is existence of natural clusters in the graph in the case that a document comprises a collection of sentences, we suggest distinguishing intra- and inter-document sentence relations in order to take into consideration the influence of cluster (i.e. document) global information on local sentence evaluation. In our model, five kinds of relations are involved among the three objects, i.e. document, sentence and query. Three of them are new and normally ignored in previous graph-based models. All these relations are then appropriately formulated in the QoCsR algorithm though in different ways. ROUGE evaluations shows that QoCsR can outperform the best DUC 2005 participating systems.
KW - Graph model and ranking algorithm
KW - Multi-document summarization
KW - Query-oriented summarization
UR - http://www.scopus.com/inward/record.url?scp=41849103431&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-78646-7_42
DO - 10.1007/978-3-540-78646-7_42
M3 - Conference article published in proceeding or book
SN - 3540786457
SN - 9783540786450
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 446
EP - 453
BT - Advances in Information Retrieval - 30th European Conference on IR Research, ECIR 2008, Proceedings
T2 - 30th Annual European Conference on Information Retrieval, ECIR 2008
Y2 - 30 March 2008 through 3 April 2008
ER -