When information is abundant, it becomes increasingly difficult to fit nuggets of knowledge into a single coherent picture. Complex stories spaghetti into branches, side stories, and intertwining narratives; search engines, our most popular navigational tools, are limited in their capacity to explore such complex stories. We propose a methodology for creating structured summaries of information, which we call metro maps. Our proposed algorithm generates a concise structured set of documents that maximizes coverage of salient pieces of information. Most importantly, metro maps explicitly show the relations among retrieved pieces in a way that captures story development. The overarching theme of this work is formalizing characteristics of good maps, and providing efficient algorithms (with theoretical guarantees) to optimize them. Moreover, as information needs vary from person to person, we integrate user interaction into our framework, allowing users to alter the maps to better reflect their interests. Pilot user studies with real-world datasets demonstrate that the method is able to produce maps which help users acquire knowledge efficiently. We believe that metro maps could be powerful tools for any Web user, scientist, or intelligence analyst trying to process large amounts of data.