A cluster-sensitive graph model for query-oriented multi-document summarization

Authors:
Furu Wei;Wenjie Li;Qin Lu;Yanxiang He
Affiliations:
Department of Computer Science and Technology, Wuhan University, China and Department of Computing, The Hong Kong Polytechnic University, Hong Kong;Department of Computing, The Hong Kong Polytechnic University, Hong Kong;Department of Computing, The Hong Kong Polytechnic University, Hong Kong;Department of Computer Science and Technology, Wuhan University, China
Venue:
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
Year:
2008

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Abstract

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.