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.

本研究引入了 “点击诱饵破坏” 这一新技术，旨在检测、分类和生成剧透作为简洁的文本响应，以应对点击诱饵内容引发的好奇心。通过利用多任务学习框架，我们的模型的泛化能力得到了显著提升，有效解决了点击诱饵的普遍问题。我们研究的关键是生成适当的剧透，无论是短语、扩展段落还是多个剧透，具体取决于所需的剧透类型。我们的方法结合了两个关键技术：一个经过精细调整的剧透分类方法和一个修改过的问题回答机制，这两者都被纳入到多任务学习范式中，以优化从上下文中提取剧透。值得注意的是，我们还包含了对能处理更长序列的模型进行微调的方法，以适应扩展剧透的生成。本研究突出了复杂文本处理技术在解决点击诱饵这一无处不在的问题上的潜力，为数字领域的用户体验提供了改进。