Query-based multi-document summarization aims to create a short summary given a collection of documents and a query. Most of the existing methods treat the query as one single sentence and rank the sentences in the documents based on their similarities with the query sentence. However, these methods lack of intensive analysis on the given query which typically consist of several topic aspects. In this paper, we propose a topic aspect extraction method to discover the aspect words and sentences contained in the query narrative texts and the input documents, and then incorporate these aspect words and sentences into a cross propagation model based on the sentence-term bipartite graph for document summarization. Experiments on DUC benchmark data show the effectiveness of our proposed approach on the topic-driven document summarization task.