Computer Science ›› 2013, Vol. 40 ›› Issue (5): 201-205.

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Partial Absorbing Markov Chain Based Multi-document Summarization

GAO Jing and FANG Jun   

  • Online:2018-11-16 Published:2018-11-16

Abstract: 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.The equivalent relationship between the average expected visits in Absorbing Markov Chain and the stationary probability in the corresponding Partial Absorbing Markov Chain was demonstrated.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 TAC2011main task are performed.

Key words: Partial absorbing markov chain,LexRank,Topic-oriented prior distribution,Multi-document summarization

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