Abstract
In this paper, we present a new corpus of clickbait articles annotated by university students along with a corresponding shared task: clickbait articles use a headline or teaser that hides information from the reader to make them curious to open the article. We therefore propose to construct approaches that can automatically extract the relevant information from such an article, which we call clickbait resolving. We show why solving this task might be relevant for end users, and why clickbait can probably not be defeated with clickbait detection alone. Additionally, we argue that this task, although similar to question answering and some automatic summarization approaches, needs to be tackled with specialized models. We analyze the performance of some basic approaches on this task and show that models fine-tuned on our data can outperform general question answering models, while providing a systematic approach to evaluate the results. We hope that the data set and the task will help in giving users tools to counter clickbait in the future.- Anthology ID:
- 2022.lrec-1.54
- Volume:
- Proceedings of the Thirteenth Language Resources and Evaluation Conference
- Month:
- June
- Year:
- 2022
- Address:
- Marseille, France
- Editors:
- Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 515–523
- Language:
- URL:
- https://aclanthology.org/2022.lrec-1.54
- DOI:
- Cite (ACL):
- Benjamin Hättasch and Carsten Binnig. 2022. Know Better – A Clickbait Resolving Challenge. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 515–523, Marseille, France. European Language Resources Association.
- Cite (Informal):
- Know Better – A Clickbait Resolving Challenge (Hättasch & Binnig, LREC 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/2022.lrec-1.54.pdf
- Data
- SQuAD