Topic summarization is a natural language processing for creating summaries of topic information. Previous work focused on summaries of news, web documents and blogs, while seldom on microblog topic summaries. A microblog topic summarization (MTS) method is proposedbased on topology structures for microblog retweets. First, representative terms are selected according to structural relationships between retweeting tweets. Second, topic areas are identified after topic nodes are merged by using depth-first and breath-frist methods. Third, topic-oriented summaries with topology structure are generated through measuring adjacent topic nodes on the retweeting graph. Finally, experiments on the real world event datasets show the effectiveness of the proposed methods. Visual topic summary trees are also produced for remarkably emphasizing the insight behind the evolving topics.