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


全文

其它資訊
摘要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 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.
著者Bing L., Li P., Liao Y., Lam W., Guo W., Passonneau R.J.
會議名稱53rd 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
會議開始日26.07.2015
會議完結日31.07.2015
會議地點Beijing
會議國家/地區中國
詳細描述organized by Association for Computational Linguistics,
出版年份2015
月份1
日期1
卷號1
頁次1587 - 1597
國際標準書號9781941643723
語言英式英語

上次更新時間 2020-06-09 於 00:49