Abstractive Multi-Document Summarization via Phrase Selection and Merging

Lidong Bing, Piji Li, Yi Liao, Wai Lam, Weiwei Guo, Rebecca Passonneau
2015 Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)  
We propose an abstraction-based multidocument summarization framework that can construct new sentences by exploring more fine-grained syntactic units than sentences, namely, noun/verb phrases. Different from existing abstraction-based approaches, our method first constructs a pool of concepts and facts represented by phrases from the input documents. Then new sentences are generated by selecting and merging informative phrases to maximize the salience of phrases and meanwhile satisfy the
more » ... e construction constraints. We employ integer linear optimization for conducting phrase selection and merging simultaneously in order to achieve the global optimal solution for a summary. Experimental results on the benchmark data set TAC 2011 show that our framework outperforms the state-ofthe-art models under automated pyramid evaluation metric, and achieves reasonably well results on manual linguistic quality evaluation.
doi:10.3115/v1/p15-1153 dblp:conf/acl/BingLLLGP15 fatcat:zbbnkk5aojdprm2fzv6i4o5fmu