  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 documents. 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.
