Mitigating Clickbait: An Approach to Spoiler Generation Using Multitask Learning

arXiv:2405.04292v1 Announce Type: new
Abstract: This study introduces 'clickbait spoiling', a novel technique designed to detect, categorize, and generate spoilers as succinct text responses, countering the curiosity induced by clickbait content. By leveraging a multi-task learning framework, our model's generalization capabilities are significantly enhanced, effectively addressing the pervasive issue of clickbait. The crux of our research lies in generating appropriate spoilers, be it a phrase, an extended passage, or multiple, depending on the spoiler type required. Our methodology integrates two …

abstract arxiv capabilities clickbait cs.ai cs.cl curiosity framework generate issue multi-task learning multitask learning novel responses study text type