A Scalable MMR Approach to Sentence Scoring for Multi-Document Update Summarization
نویسندگان
چکیده
We present SMMR, a scalable sentence scoring method for query-oriented update summarization. Sentences are scored thanks to a criterion combining query relevance and dissimilarity with already read documents (history). As the amount of data in history increases, non-redundancy is prioritized over query-relevance. We show that SMMR achieves promising results on the DUC 2007 update corpus.
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تاریخ انتشار 2008