This paper presents a system to summarize a microblog post and its responses with the goal to provide readers a more constructive and concise set of information for efficient digestion. We introduce a novel two-phase summarization scheme. In the first phase, the post plus its responses are classified into four categories based on the intention, Interrogation, URL-Sharing, URL-Discussion and Chat. For each type of post, in the second phase, we exploit different strategies, including Opinion Analysis, Response Group Clustering, and Response Relevancy Detection, to summarize and highlight critical information to display. This system provides an alternative thinking about machine-summarization: by utilizing AI approaches, computers are capable of constructing deeper and more user-friendly abstraction.