Until recently there are no common, convenient, and repeatable evaluation methods that could be easily applied to support fast turn-around development of automatic text summarization systems. In this paper, we introduce an information theoretic approach to automatic evaluation of summaries based on the Jensen-Shannon divergence of distributions between an automatic summary and a set of reference summaries. Several variants of the approach are also considered and compared. The results indicate that JS divergence based evaluation method achieves comparable performance with the common automatic evaluation method ROUGE in single documents summarization task; while achieves better performance than ROUGE in multiple document summarization task.