| dc.description.abstract | Information technology has developed rapidly in recent years, and the number of electronic documents has increased, too. To avoid readers spend too much time realizing the content of article, it’s useful to help them understand quickly that extracting important sentences and then making summarization. However, the traditional extracting method only judges whether the sentences contain the important terms or not, and it doesn’t use the concept of topic, either. In addition, the traditional extracting method also doesn’t focus on the whole news event to make a comprehensive explanation. This paper uses Graph-based Summarization method to extract multi-document summarization, which is a kind of Indicator representation approaches to divide document in smaller fragment, and this study uses sentence to represent it. After using smaller fragment to build Graph-based network, this paper uses clustering and many kinds of link analysis methods to score the nodes. After that, this study takes cluster weight into consideration for scoring and uses the sentence nodes to make summarization.
The experiment uses DUC 2002 and TAC 2010 dataset, and uses ROUGE to evaluation the quality of summarization. The result shows that all the methods can reach a well level. The ROUGE-1 score of DUC 2002 50 words and 100 words can reach 0.2996 and 0.3412, it approximate to the peers in DUC 2002. The ROUGE-1 score of the first part of TAC 2010 Guided Summarization can reach 0.3513, and it’s higher than other peers. Finally, the ROUGE-2 score can reach 0.0707, it also has medium quality.
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