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The most important step of query-focused extractive summarization is deciding which sentences are appropriately included in the final summary. In this paper, we propose a feature fusion based sentence selecting strategy, to identify the sentences with high query-relevance and high information density. We score each sentence by computing its similarity and Skip-Bigram co-occurrence with query. These two features can measure the query-relevance from content and structure respectively. Then, we re-score the sentences using the information density feature gained from a text graph which can provide position information. And finally, we adopt MMR for sentence extracting. Experimental results indicate that this method is effective in capturing important sentences. The ROUGE-2 and ROUGE-SU4 scores are 0.0640 and 0.1233, which are at the top of the DUC2005 scores.