摘要：We present a novel unsupervised integrated score framework to generate generic extractive multi-document summaries by ranking sentences based on dynamic programming (DP) strategy. Considering that cluster-based methods proposed by other researchers tend to ignore informativeness of words when they generate summaries, our proposed framework takes relevance, diversity, informativeness and length constraint of sentences into consideration comprehensively. We apply Density Peaks Clustering (DPC) to get relevance scores and diversity scores of sentences simultaneously. Our framework produces the best performance on DUC2004, 0.396 of ROUGE-1 score, 0.094 of ROUGE-2 score and 0.143 of ROUGE-SU4 which outperforms a series of popular baselines, such as DUC Best, FGB [7], and BSTM [10].
关键词：Multi-document summarization; Integrated score framework; Density peaks clustering; Sentences rank
