In order to produce summaries of online comment text

this paper presents a novel sentiment summarization framework which can produce abstractive summary based on Opinosis graph and Markov random walk model.This framework first convert the original text into Opinosis graph and use the Opinosis graph to mine the features of the original text

which can be used to classify the sentences.And then this paper adds a sentiment layer upon the cluster-based conditional Markov random walk nodel

and this improved model can judge which sentiment polar of the sentences in the same cluster is representative and select the proper sentence to produce abstractive summary based on the factors of sentiment and cluster.Experimental results show that this framework has achieved better results in ROUGE(Recall-Oriented Understudy for Gisting Evaluation)value compared to the baseline algorithm.