News Article Teaser Tweets and How to Generate Them
Sanjeev Kumar Karn, Mark Buckley, Ulli Waltinger, Hinrich Sch\"utze

TL;DR
This paper introduces the task of teaser generation for news articles, providing a new dataset, evaluation benchmark, and baseline neural models to generate engaging, curiosity-arousing teasers for social media sharing.
Contribution
It defines teaser generation as a new task, creates a novel dataset, and benchmarks neural models, highlighting the effectiveness of seq2seq with pointer network.
Findings
Seq2seq with pointer network performs best
New dataset of teasers compiled from tweets
Baseline systems established for future research
Abstract
In this work, we define the task of teaser generation and provide an evaluation benchmark and baseline systems for the process of generating teasers. A teaser is a short reading suggestion for an article that is illustrative and includes curiosity-arousing elements to entice potential readers to read particular news items. 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 those that conform to the teaser definition. We have compared 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.
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Code & Models
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Taxonomy
TopicsTopic Modeling · Advanced Text Analysis Techniques · Sentiment Analysis and Opinion Mining
MethodsSigmoid Activation · Tanh Activation · Long Short-Term Memory · Sequence to Sequence
