Mitigating Clickbait: An Approach to Spoiler Generation Using Multitask Learning

TL;DR

This paper presents a multi-task learning approach to detect, categorize, and generate spoilers to counteract clickbait, improving user experience by providing relevant, succinct spoiler responses.

Contribution

Introduces a novel multi-task learning framework that enhances spoiler detection, categorization, and generation, including techniques for handling longer sequences for extended spoilers.

Findings

Effective spoiler generation across different types

Improved clickbait mitigation through multi-task learning

Enhanced model generalization capabilities

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 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.