Mitigating Clickbait: An Approach to Spoiler Generation Using Multitask Learning
This addresses the problem of clickbait for digital media users, representing an incremental improvement in text processing techniques.
This paper tackles clickbait by developing a system to generate spoilers (succinct text responses) using a multi-task learning framework that integrates spoiler categorization and a modified question answering mechanism, achieving enhanced generalization capabilities.
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 crucial techniques: a refined spoiler categorization method and a modified version of the Question Answering (QA) mechanism, incorporated within a multi-task learning paradigm for optimized spoiler extraction from context. Notably, we have included fine-tuning methods for models capable of handling longer sequences to accommodate the generation of extended spoilers. This research highlights the potential of sophisticated text processing techniques in tackling the omnipresent issue of clickbait, promising an enhanced user experience in the digital realm.