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When people recall and digest what they have read for writing summaries, the important content is more likely to attract their attention. Inspired by this observation, we propose a cascaded attention based unsupervised model to estimate the salience information from the text for compressive multi-document summarization. The attention weights are learned automatically by an unsupervised data reconstruction framework which can capture the sentence salience. By adding sparsity constraints on thedoi:10.18653/v1/d17-1221 dblp:conf/emnlp/LiLBGL17 fatcat:liis25rfrfcyvn4372ybikfw3u