Recently, much study has been directed toward summarizing event data, in the hope that the summary will lead us to a better understanding of the system that generates the events. However, instead of offering a global picture of the system, the summary obtained by most current approaches are piecewise, each describing an isolated snapshot of the system. We argue that the best summary, both in terms of its minimal description length and its interpretability, is the one obtained with the understanding of the internal dynamics of the system. Such understanding includes, for example, what are the internal states of the system, and how the system alternates among these states. In this paper, we adopt an algorithmic approach for event data summarization. More specifically, we use a hidden Markov model to describe the event generation process. We show that summarizing events based on the learned hidden Markov Model achieves short description length and high interpretability. Experiments show that our approach is both efficient and effective.