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

Author(s)
Szymanski, Terrence  
Orellana-Rodriguez, Claudia  
Keane, Mark T.  
Uri
http://hdl.handle.net/10197/7971
Date Issued
2016-07-10
Date Available
2016-09-20T10:13:39Z
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.
Sponsorship
Science Foundation Ireland
Type of Material
Conference Publication
Copyright (Published Version)
© 2016 International Joint Conferences on Artificial Intelligence
Subjects

Machine learning

Statistics

Keyword analysis

Web versions
http://ijcai-16.org/index.php/welcome/view/home
Language
English
Status of Item
Peer reviewed
Journal
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
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
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insight_publication.pdf

Size

604.03 KB

Format

Adobe PDF

Checksum (MD5)

38c3a0af22486ae5d63d27cc337a4e79

Owning collection
Insight Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
All other content is subject to copyright.

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