Decayed DivRank for Guided Summarization
نویسندگان
چکیده
Guided summarization is essentially an aspect-based multi-document summarization, where aspects can be taken as specified queries in summarization. We proposed a novel ranking algorithm, Decayed DivRank (DDRank) for guided summarization tasks of TAC2011. DDRank can address relevance, importance, diversity, and novelty simultaneously through a decayed vertex-reinforced random walk process in sentence ranking. With sentence ranked by DDRank, top-ranked sentences are supposed to be able to present aspect-related, important, diverse, and novel information contained in documents. Hence, we can select the top-ranked sentences as summary candidates directly. Aspects are approximately obtained by pLSA, and are adopted to generate sentence prior for DDRank in our approach.
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