Clickbait Spoiling via Question Answering and Passage Retrieval

Matthias Hagen; Maik Fröbe; Artur Jurk; Martin Potthast

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arXiv · 2203.10282

Clickbait Spoiling via Question Answering and Passage Retrieval

Matthias Hagen·Maik Fröbe·Artur Jurk·Martin Potthast

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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.

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Matthias Hagen, Maik Fröbe, Artur Jurk, Martin Potthast. 2022-03-19. Clickbait Spoiling via Question Answering and Passage Retrieval. https://arxiv.org/abs/2203.10282

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