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We present a novel framework of system combination for multi-document summarization. For each input set (input), we generate candidate summaries by combining whole sentences from the summaries generated by different systems. We show that the oracle among these candidates is much better than the summaries that we have combined. We then present a supervised model to select among the candidates. The model relies on a rich set of features that capture content importance from different perspectives.doi:10.18653/v1/d15-1011 dblp:conf/emnlp/HongMN15 fatcat:ykbw4th2q5bk7o64gb37f3fyze