The implementation of a query-directed multi-document

Query-directed multi-document summarization aims to provide a more effective characterization of a document set accounting to the user's information need when generating a summary. In this paper, we propose a practical approach for this task by identifying the sentences with high query-relevant and high information density. This is implemented by mining two kinds of features for each sentence: the power of correlation with the query and the power of global connectivity. While the first is executed by computing semantic similarity between the sentence and the query, and the other is executed by using semantic graph. Then these two kinds of features are blessed to score each sentence. At last with the help of MMR for reducing redundancy, we get the summary. Experimental results indicate that this method is encouraging for both those retrieved documents that correspondingly concentrating to one subject and retrieved documents who have many sub-topics and comparatively being related to the query.