一种基于Opinosis图和马尔科夫随机游走模型的多文本情感摘要框架

针对在线文本情感摘要生成问题，本文提出了一种基于Opinosis图和马尔科夫随机游走模型的情感摘要框架.首先，该框架将原始文本转化为Opinosis图，并利用其挖掘出文本中的特征词，这些特征词可以用来对原始文本的句子进行分类；其次本文在基于聚类的条件马尔科夫随机游走模型的基础上增加了情感层，改进后的模型可以判断同一聚类中各句子的情感倾向是否具有代表性并结合情感和聚类信息对句子进行排序.实验结果表明，本文提出的方法与基准算法相比在ROUGE（Recall-Oriented Understudy for Gisting Evaluation）值上具有明显提高.

An Opinosis and MRW Based Sentiment Summarization Framework

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.