News Article Teaser Tweets and How to Generate Them

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.