NAACL-HLT 2019: News Article Teaser Tweets and How to Generate Them, Minneapolis, USA, June 2nd, 2019

NAACL-HLT 2019: News Article Teaser Tweets and How to Generate Them, Minneapolis, USA, June 2nd, 2019

Abstract:

We define the task of teaser generation and provide an evaluation benchmark and baseline systems for it. A teaser is a short reading suggestion for an article that is illustrative and includes curiosity-arousing elements to entice potential readers to read the news item. Teasers are one of the main vehicles for transmitting news to social media users. We compile a novel dataset of teasers by systematically accumulating tweets and selecting ones that conform to the teaser definition. We compare a number of neural abstractive architectures on the task of teaser generation and the overall best performing system is See et al.(2017)’s seq2seq with pointer network.

Title: News Article Teaser Tweets and How to Generate Them
Authors:  Sanjeev Karn, Mark Buckley, Ulli Waltinger, Hinrich Schütze
Pub/Conf: North American Chapter of the Association for Computational Linguistics (NAACL), Minneapolis, USA, June 2019

BibTeX:

 
@article{DBLP:journals/corr/abs-1807-11535, author = {Sanjeev Kumar Karn and Mark Buckley and Ulli Waltinger and Hinrich Sch{\"{u}}tze}, title = {News Article Teaser Tweets and How to Generate Them}, journal = {CoRR}, volume = {abs/1807.11535}, year = {2018}, url = {http://arxiv.org/abs/1807.11535}, archivePrefix = {arXiv}, eprint = {1807.11535}, timestamp = {Mon, 13 Aug 2018 16:46:24 +0200}, biburl = {https://dblp.org/rec/bib/journals/corr/abs-1807-11535}, bibsource = {dblp computer science bibliography, https://dblp.org} }

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