# News Article Teaser Tweets and How to Generate Them

arXiv: https://arxiv.org/abs/1807.11535 · HTML: https://mlsanity.com/paper/1807.11535/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jul 2018.

## Built on by (most cited first, 2 of 2)

- [Clickbait Spoiling via Question Answering and Passage Retrieval](https://mlsanity.com/paper/2203.10282/) (2022)
- [Introducing Spotlight: A Novel Approach for Generating Captivating Key Information from Documents](https://mlsanity.com/paper/2509.10935/) (2025)

## Abstract (arXiv)

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.