It is commonly agreed that we are in the era of big data (Wang et al. 2015). Among various types of data, texts are the most common and pervasive all over the network. Although many effective technologies such as distributive or parallel computations have been proposed, e.g., MapReduce (Slagter et al. 2013; Salgter, et al. 2015; Salgter, et al. 2015), the information overload problem is getting worse as the quantity of data keep increasing rapidly. Automatic text summarization arises as an effective technology for producing a concise and fluent summary conveying the key information in the original text document (Nenkova & McKeown, 2012). Currently, high performance automatic summarization has already become a very important topic in the area of machine learning and data mining, and it is widely used in a large number of industrial sectors, especially in search engines such as Google, Baidu, Yahoo and news portals such as BBC, CNN and NBC News. Many researchers have developed various word-based, sentence-based and graph-based summarization methods. Among them, graph-based methods have attracted a lot of attentions. For example, Ferreira et al. (2013) proposed a four-dimension (including similarity, semantic similarity, co-reference and discourse information) graph model by taking co-reference resolution and the role of pronouns in connecting the sentences into consideration. See (Gupta &Lehal, 2010) and (Joshi & Sonawane, 2015) for more detailed surveys of extractive summarization techniques and graph-based methods.