Supervised machine learning addresses the problem of approximating a function, given the examples of inputs and outputs. The classical tasks of regression and classification deal with functions whose outputs are real numbers. Structured output prediction goes beyond one-dimensional outputs, and allows predicting complex objects, such as sequences, trees, and graphs. In this talk I will show how to apply structured output prediction to building informative summaries of the topic graphs—a problem I encountered in my Ph.D. research. The focus of the talk will be on understanding the intuitions behind the machine learning algorithms. We will start from the basics and walk our way through the inner workings of DAgger—state-of-the-art method of structured output prediction. This talk was be given at a seminar in Google Krakow.