We describe a learning-based system for generating reports based on a mix of text and event data. The system
incorporates several stages of processing, including aggregation, template-filling and importance ranking.
Aggregators and templates were based on a corpus of reports evaluated by human judges. Importance and granularity
were learned from this corpus as well. We find that high-scoring reports (with a recall of 0.89) can be reliably
produced using this procedure given a set of oracle features. The report drafting system is part of a learning cognitive
assistant RADAR, and is used to describe its performance.