Helping News Editors Write Better Headlines: A Recommender to Improve the Keyword Contents & Shareability of News Headlines
This work addresses the specific problem of improving headline quality and shareability for news editors, representing an incremental application of existing methods to a new domain.
The paper tackles the problem of helping news editors write effective headlines by developing a software tool that uses NLP and machine learning to recommend salient keywords and predict social media shareability, providing an efficient way to combine automated predictors with human judgment.
We present a software tool that employs state-of-the-art natural language processing (NLP) and machine 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 regression model to identify headlines that are likely to be widely shared on social media. The user interface is designed to simplify and speed the editor's decision process on the composition of the headline. 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.