Timeline summarization is the process of creating summaries towards topic information and development over time in natural language processing. Some algorithms are proposed to generate summaries towards long text like news, but seldom focus on timeline summaries of short text like microblog. Here, we propose a microblog timeline summarization based on sliding window (MTSW), which simultaneously incorporates content coverage, temporal distribution and influence to evaluate candidate timeline summaries. In the algorithm, representative terms are selected to represent microblog feature according to intensity of terms and entropy. We build a comprehensive indicator for evaluating the timeline summary based on the above three indicators. Then, we use sliding window to generate microblog timeline summary. Experiments on the real-world event datasets verify the effectiveness of the proposed method.