Helping News Editors Write Better Headlines: A Recommender to Improve the Keyword Contents and Shareability of News Headlines

We present a software tool that employs state-of- the-art natural language processing (NLP) and ma- chine learning techniques to help newspaper editors compose effective headlines for online publication. The system identifies the most salient keywords in a news article and ranks them based on both their overall popularity and their direct relevance to the article. The system also uses a supervised regres- sion model to identify headlines that are likely to be widely shared on social media. The user inter- face is designed to simplify and speed the editor’s decision process on the composition of the head- line. As such, the tool provides an efficient way to combine the benefits of automated predictors of engagement and search-engine optimization (SEO) with human judgments of overall headline quality.