News archives are an invaluable primary source for placing current events in historical context. But current search engine tools do a poor job at uncovering broad themes and narratives across docu- ments. We present Rookie: a practical software system which uses natural language processing (NLP) to help readers, reporters and editors uncover broad stories in news archives. Unlike prior work, Rookie’s design emerged from 18 months of iterative development in consultation with editors and computational journalists. This process lead to a dramatically different approach from previous academic systems with similar goals. Our efforts offer a generalizable case study for others building real-world journalism software using NLP.