acl acl2013 acl2013-377 acl2013-377-reference knowledge-graph by maker-knowledge-mining
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Author: Chen Li ; Xian Qian ; Yang Liu
Abstract: In this paper, we propose a bigram based supervised method for extractive document summarization in the integer linear programming (ILP) framework. For each bigram, a regression model is used to estimate its frequency in the reference summary. The regression model uses a variety ofindicative features and is trained discriminatively to minimize the distance between the estimated and the ground truth bigram frequency in the reference summary. During testing, the sentence selection problem is formulated as an ILP problem 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 conducted various analysis to show the impact of bigram selection, weight estimation, and ILP setup.
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