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

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Title: Helping News Editors Write Better Headlines: A Recommender to Improve the Keyword Contents and Shareability of News Headlines
Authors: Szymanski, Terrence
Orellana-Rodriguez, Claudia
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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