Multi-document summarization using A* search and discriminative training
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 of the 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.