Using Supervised Bigram-based ILP for Extractive Summarization

Chen Li, Xian Qian and Yang Liu

The 51st Annual Meeting of the Association for Computational Linguistics (ACL 2013)
Sofia, Bulgaria, August 4-9, 2013

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Abstract

In this paper, we propose a bigram based supervised method for extractive docu- ment summarization in the integer linear programing (ILP) framework. For each bi- gram, a regression model is used to esti- mate its frequency in the reference sum- mary. The regression model uses a vari- ety of indicative features and is trained dis- criminatively to minimize the distance be- tween the estimated and the ground truth bigram frequency in the reference sum- mary. During testing, the sentence selec- tion problem is formulated as an ILP prob- lem to maximize the bigram gains. We demonstrate that our system consistently outperforms the previous ILP method on different TAC data sets, and performs competitively compared to the best results in the TAC evaluations. We also con- ducted various analysis to show the im- pact of bigram selection, weight estima- tion, and ILP setup.

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