Paper: Using Supervised Bigram-based ILP for Extractive Summarization

ACL ID P13-1099
Title Using Supervised Bigram-based ILP for Extractive Summarization
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2013
Authors

In this paper, we propose a bigram based supervised method for extractive docu- ment summarization in the integer linear programming (ILP) framework. For each bigram, a regression model is used to es- timate 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...