As more and more people are expected to work with complex AI-systems, it becomes more important than ever that such systems provide intuitive explanations for their decisions. A prerequisite for holding such explanatory dialogue is the ability of the systems to present their proposed decisions to the user in an easy-to-understand form. Unfortunately, such dialogues could become hard to facilitate in real-world problems where the system may be planning for multiple eventualities in stochastic environments. This means for the system to be effective, it needs to be able to present the policy at a high-level of abstraction and delve into details as required. Towards this end, we investigate the utility of temporal abstractions derived through analytically computed landmarks and their relative ordering to build a summarization of policies for Stochastic Shortest Path Problems. We formalize the concept of policy landmarks and show how it can be used to provide a high level overview of a given policy. Additionally, we establish the connections between the type of hierarchy we generate and previous works in temporal abstractions, specifically MaxQ hierarchies. Our approach is evaluated through user studies as well as empirical metrics that establish that people tend to choose landmarks facts as subgoals to summarize policies and demonstrates the performance of our approach on standard benchmarks.