Clickbait Spoiling via Question Answering and Passage Retrieval
Matthias Hagen, Maik Fröbe, Artur Jurk, Martin Potthast
Abstract
We introduce and study the task of clickbait spoiling: generating a short text that satisfies the curiosity induced by a clickbait post. Clickbait links to a web page and advertises its contents by arousing curiosity instead of providing an informative summary. Our contributions are approaches to classify the type of spoiler needed (i.e., a phrase or a passage), and to generate appropriate spoilers. A large-scale evaluation and error analysis on a new corpus of 5,000 manually spoiled clickbait posts—the Webis Clickbait Spoiling Corpus 2022—shows that our spoiler type classifier achieves an accuracy of 80%, while the question answering model DeBERTa-large outperforms all others in generating spoilers for both types.
BibTeX
@inproceedings{hagen-etal-2022-clickbait,
title = "Clickbait Spoiling via Question Answering and Passage Retrieval",
author = {Hagen, Matthias and
Fr{\"o}be, Maik and
Jurk, Artur and
Potthast, Martin},
editor = "Muresan, Smaranda and
Nakov, Preslav and
Villavicencio, Aline",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-long.484/",
doi = "10.18653/v1/2022.acl-long.484",
pages = "7025--7036"
}