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We present a software tool that employs state-of-the-art natural languageprocessing (NLP) and machine learning techniques to help newspaper editorscompose effective headlines for online publication. The system identifies themost salient keywords in a news article and ranks them based on both theiroverall popularity and their direct relevance to the article. The system alsouses a supervised regression model to identify headlines that are likely to bewidely shared on social media. The user interface is designed to simplify andspeed the editor's decision process on the composition of the headline. Assuch, the tool provides an efficient way to combine the benefits of automatedpredictors of engagement and search-engine optimization (SEO) with humanjudgments of overall headline quality.

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