Using Biased Random Walks for Focused Summarization
نویسنده
چکیده
We introduce a graph-based sentence ranking algorithm for extractive summarization. Our method is a version of the LexRank algorithm we introduced in DUC 2004 extended to the focused summarization task of DUC 2006. As in LexRank, we represent the set of sentences in a document cluster as a graph, where nodes are sentences and links between the nodes are induced by a similarity relation between the sentences. Then we rank the sentences according to a random walk model defined in terms of both the inter-sentence similarities and the similarities of the sentences to the topic description.
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تاریخ انتشار 2006