Rookie: A unique approach for exploring news archives
This work addresses a specific need for readers, reporters, and editors in journalism by providing a tool to explore news archives more effectively, though it appears incremental as it builds on prior academic systems with a focus on real-world application.
The authors tackled the problem of uncovering broad themes and narratives in news archives, which current search engines handle poorly, by developing Rookie, a practical NLP-based software system designed through iterative consultation with editors and journalists.
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.