Microblogs are one of the most important resources for natural language processing. This paper describes a summarization task of sports events on Twitter. We focus on an abstractive approach based on sub-events in the sports event. Abstractive summaries usually are better than summaries generated by extractive approaches in terms of readability. Furthermore, our method can incorporate sophisticated phrases that explain the scene. First, our method detects burst situations in which many users post tweets when a sub-event in a game occurs. Tweets in the burst situations are the inputs of our method. Next, it extracts sub-event elements (SEEs) that contain actions in a game, such as “Player A made a pass to Player B” and “Player B made a shot on goal.” Then, it identifies the optimal order of the extracted SEEs by using a scoring method. Finally, it generates an abstractive summary on the basis of the ordered SEEs, such as “Playler B made a shot on goal from the Player A’s pass.” In addition, it adds game-changing phrases into the abstractive summary by some rules. In the experiment, we show the effectiveness of our method as compared with related work based on an extractive approach.