Mitigating Clickbait: An Approach to Spoiler Generation Using Multitask Learning

TL;DR

The paper tackles the problem of clickbait by introducing clickbait spoiling, a task that detects spoiler type (phrase, passage, multi) and generates truthful spoilers to counter misleading headlines. It proposes a multitask learning framework that jointly handles spoiler classification and QA-based spoiler generation, augmented by context reduction via BM25 and long-sequence generation with LongT5. The study demonstrates that RoBERTa-Large achieves state-of-the-art spoiler classification accuracy and that LongT5 excels at longer spoiler generation, with Multitask Learning providing gains over single-task baselines. It also shows that context-reduction strategies can improve generation quality and that multi-spoiler outputs are effectively produced by fine-tuned LongT5. Overall, the approach promises a more satisfying user experience in the digital information landscape by mitigating clickbait through accurate, succinct spoilers, and lays groundwork for adaptive training and statistically rigorous validation in future work.

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.

Figures (2)

• Figure 1: Examples of different categories of clickbait spoilers in Webis hagen-etal-2022-clickbait dataset.
• Figure 2: Overview of the Multitask Learning Learning Spoiler Generation Model