Most research on automatic text summarization revolves around genres with relatively well-defined structure: newswire, scientific papers, legal documents, etc. In this talk I will present an approach to summarizing short fiction – a relatively unexplored data type. The objective is a particular kind of summary – one that helps the reader decide whether she wants to read a story or not. A number of characteristics of literary texts make them a difficult target for summarization: the rate of lexical repetition is much lower, position of sentences in text is not indicative of how informative they are, the use of metaphor and figurative language is very wide-spread, to name just a few. Our approach relies on using two main types of information: the information about the main entities in stories and the information about the aspectual type of sentences. While the work is preliminary, the resulting summaries have been evaluated by 15 judges. The results suggest that the summaries are helpful for achieving the original objective.