Partial Absorbing Markov Chain based Multi-document Summarization
Absorbing Markov Chain has been proven to be effective in text summarization.However, the algorithm based on Absorbing Markov Chain is not only time-consuming due to matrix inversion but also inept to integrate other information except relationships among sentences because of the limitation of the model.This paper presents a novel multi-document summarization approach based on Partial Absorbing Markov Chain.We demonstrate the equivalent relationship between the average expected visits in Absorbing Markov Chain and the stationary probability in the corresponding Partial Absorbing Markov Chain.Then, the stationary probability in Partial Absorbing Markov Chain which is easily calculated serves as a criterion to rank sentences.In addition, other kinds of information are incorporated together to generate a more accurate solution of the stationary probability.Experiments on DLC2007 are performed and the ROUGE evaluation results show that our approach ranks between the 1st and the 2nd systems on DUC2007 main task
Absorbing Markov Chain LexRank topic-oriented prior distribution Multi-document Summarization
Huili Lin Xiaolei Wang
College of Resources and Environmental Engineering ,Shandong University of Science and Technology,Qi Department of Computer Science and Engineering,The Chinese University of Hong Kong ,Hong Kong ,China
国际会议
太原
英文
617-623
2011-02-26（万方平台首次上网日期，不代表论文的发表时间）