{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/news-article-teaser-tweets-and-how-to","title":"News Article Teaser Tweets and How to Generate Them","arxiv_id":"1807.11535","date":"2018-07-30","proceeding":"NAACL 2019 6","authors":["Sanjeev Kumar Karn","Mark Buckley","Ulli Waltinger","Hinrich Schütze"],"abstract":"In this work, we define the task of teaser generation and provide an\nevaluation benchmark and baseline systems for the process of generating\nteasers. A teaser is a short reading suggestion for an article that is\nillustrative and includes curiosity-arousing elements to entice potential\nreaders to read particular news items. Teasers are one of the main vehicles for\ntransmitting news to social media users. We compile a novel dataset of teasers\nby systematically accumulating tweets and selecting those that conform to the\nteaser definition. We have compared a number of neural abstractive\narchitectures on the task of teaser generation and the overall best performing\nsystem is See et al.(2017)'s seq2seq with pointer network.","url_abs":"http://arxiv.org/abs/1807.11535v2","url_pdf":"http://arxiv.org/pdf/1807.11535v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"news-article-teaser-tweets-and-how-to","repo_url":"https://github.com/sanjeevkrn/teaser_collect","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"news-article-teaser-tweets-and-how-to","repo_url":"https://github.com/sanjeevkrn/teaser_generate","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[],"methods":[{"method_slug":"lstm","method_name":"LSTM"},{"method_slug":"seq2seq","method_name":"Seq2Seq"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1807.11535","atlas_url":"https://app.syntology.ai/?focus=1807.11535","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}