emnlp emnlp2010 emnlp2010-82 emnlp2010-82-reference knowledge-graph by maker-knowledge-mining
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Author: Ahmet Aker ; Trevor Cohn ; Robert Gaizauskas
Abstract: In this paper we address two key challenges for extractive multi-document summarization: the search problem of finding the best scoring summary and the training problem of learning the best model parameters. We propose an A* search algorithm to find the best extractive summary up to a given length, which is both optimal and efficient to run. Further, we propose a discriminative training algorithm which directly maximises the quality ofthe best summary, rather than assuming a sentence-level decomposition as in earlier work. Our approach leads to significantly better results than earlier techniques across a number of evaluation metrics.
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