When performing a software change task, programmers expend substantial effort investigating a system's code base to find and understand just the code that is pertinent to a task-at-hand. A particularly difficult kind of code to handle during these tasks is crosscutting concern code. To help programmers handle such code, we introduce an automated approach that produces a natural language summary that describes both what the concern is and how the concern is implemented. We describe our approach and present the results of an experiment in which programmers were able to perform change tasks more efficiently and more easily with generated concern summaries than without.