  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.
