Clickbait Spoiling via Question Answering and Passage Retrieval

03/19/2022

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by   Matthias Hagen, et al.

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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 all others in generating spoilers for both types.