Helping News Editors Write Better Headlines: A Recommender to Improve the Keyword Contents and Shareability of News Headlines
|Title:||Helping News Editors Write Better Headlines: A Recommender to Improve the Keyword Contents and Shareability of News Headlines||Authors:||Szymanski, Terrence
Keane, Mark T.
|Permanent link:||http://hdl.handle.net/10197/7971||Date:||10-Jul-2016||Abstract:||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.||Funding Details:||Science Foundation Ireland||Type of material:||Conference Publication||Copyright (published version):||© 2016 International Joint Conferences on Artificial Intelligence||Keywords:||Machine learning;Statistics;Keyword analysis||Language:||en||Status of Item:||Peer reviewed||Is part of:||Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence||Conference Details:||Natural Language Processing meets Journalism IJCAI-16 Workshop, New York, United States of America, 10 July 2016|
|Appears in Collections:||Insight Research Collection|
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