@inproceedings{piotrkowicz-etal-2017-automatic,
title = "Automatic Extraction of News Values from Headline Text",
author = "Piotrkowicz, Alicja and
Dimitrova, Vania and
Markert, Katja",
editor = "Kunneman, Florian and
I{\~n}urrieta, Uxoa and
Camilleri, John J. and
Ardanuy, Mariona Coll",
booktitle = "Proceedings of the Student Research Workshop at the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics",
month = apr,
year = "2017",
address = "Valencia, Spain",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/E17-4007",
pages = "64--74",
abstract = "Headlines play a crucial role in attracting audiences{'} attention to online artefacts (e.g. news articles, videos, blogs). The ability to carry out an automatic, large-scale analysis of headlines is critical to facilitate the selection and prioritisation of a large volume of digital content. In journalism studies news content has been extensively studied using manually annotated news values - factors used implicitly and explicitly when making decisions on the selection and prioritisation of news items. This paper presents the first attempt at a fully automatic extraction of news values from headline text. The news values extraction methods are applied on a large headlines corpus collected from The Guardian, and evaluated by comparing it with a manually annotated gold standard. A crowdsourcing survey indicates that news values affect people{'}s decisions to click on a headline, supporting the need for an automatic news values detection.",
}
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<abstract>Headlines play a crucial role in attracting audiences’ attention to online artefacts (e.g. news articles, videos, blogs). The ability to carry out an automatic, large-scale analysis of headlines is critical to facilitate the selection and prioritisation of a large volume of digital content. In journalism studies news content has been extensively studied using manually annotated news values - factors used implicitly and explicitly when making decisions on the selection and prioritisation of news items. This paper presents the first attempt at a fully automatic extraction of news values from headline text. The news values extraction methods are applied on a large headlines corpus collected from The Guardian, and evaluated by comparing it with a manually annotated gold standard. A crowdsourcing survey indicates that news values affect people’s decisions to click on a headline, supporting the need for an automatic news values detection.</abstract>
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%0 Conference Proceedings
%T Automatic Extraction of News Values from Headline Text
%A Piotrkowicz, Alicja
%A Dimitrova, Vania
%A Markert, Katja
%Y Kunneman, Florian
%Y Iñurrieta, Uxoa
%Y Camilleri, John J.
%Y Ardanuy, Mariona Coll
%S Proceedings of the Student Research Workshop at the 15th Conference of the European Chapter of the Association for Computational Linguistics
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F piotrkowicz-etal-2017-automatic
%X Headlines play a crucial role in attracting audiences’ attention to online artefacts (e.g. news articles, videos, blogs). The ability to carry out an automatic, large-scale analysis of headlines is critical to facilitate the selection and prioritisation of a large volume of digital content. In journalism studies news content has been extensively studied using manually annotated news values - factors used implicitly and explicitly when making decisions on the selection and prioritisation of news items. This paper presents the first attempt at a fully automatic extraction of news values from headline text. The news values extraction methods are applied on a large headlines corpus collected from The Guardian, and evaluated by comparing it with a manually annotated gold standard. A crowdsourcing survey indicates that news values affect people’s decisions to click on a headline, supporting the need for an automatic news values detection.
%U https://aclanthology.org/E17-4007
%P 64-74
Markdown (Informal)
[Automatic Extraction of News Values from Headline Text](https://aclanthology.org/E17-4007) (Piotrkowicz et al., EACL 2017)
ACL
- Alicja Piotrkowicz, Vania Dimitrova, and Katja Markert. 2017. Automatic Extraction of News Values from Headline Text. In Proceedings of the Student Research Workshop at the 15th Conference of the European Chapter of the Association for Computational Linguistics, pages 64–74, Valencia, Spain. Association for Computational Linguistics.