Query-Based Summarization for Search Lists

In this paper we describe a query-based summarization system. We propose a practical approach for this task by identifying the sentences with high query-relevant and high information density. This paper introduces a statistical model for query-relevant summarization: adopt Word overlap feature to capture the power of correlation with the query and mine the relations between the sentences. 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 subtopics and comparatively being related to the query.