News Article Teaser Tweets and How to Generate Them
This work addresses the need for automated teaser generation to attract readers on social media, but it is incremental as it builds on existing methods without introducing new techniques.
The authors tackled the problem of generating teaser tweets for news articles by defining the task, creating a benchmark dataset, and evaluating neural abstractive models, with the best system being a seq2seq with pointer network from prior work.
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.