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.
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