A sentence selection method of query-based Chinese Multi-document summarization

With the Internet developing, there are many documents on the same topic which contains redundance. Multi-Document summarization is a technology of natural languages processing, which extract important information from multiple texts about the same topic according to the compression ratio. Sentence selection is an important part of Multi-document summarization .In this paper, we design a calculate method for Chinese words semantic similarity based on Hownet and Tongyici Cilin and we also design a calculate method for sentences semantic similarity based on N-gram. Using these methods, we evaluate the importance of each candidate sentence based on exploiting both the feature of correlation with the query and the feature of the global association feature. We use an improved MMR method to select the sentence in order to reducing the redundancy. The evaluation and results are presented, which prove that the proposed methods are efficient and the summaries generated are good.