Abstractive multi-document summarization via phrase selection and merging
Refereed conference paper presented and published in conference proceedings


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AbstractWe 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 sentence 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.
All Author(s) ListBing L., Li P., Liao Y., Lam W., Guo W., Passonneau R.J.
Name of Conference53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, ACL-IJCNLP 2015
Start Date of Conference26/07/2015
End Date of Conference31/07/2015
Place of ConferenceBeijing
Country/Region of ConferenceChina
Detailed descriptionorganized by Association for Computational Linguistics,
Year2015
Month1
Day1
Volume Number1
Pages1587 - 1597
ISBN9781941643723
LanguagesEnglish-United Kingdom

Last updated on 2020-23-05 at 00:30