Clickbait spoiling aims to generate a short text to satisfy the curiosity induced by a clickbait post. As it is a newly introduced task, the dataset is only available in English so far. Our contributions include the construction of manually labeled clickbait spoiling corpus in Indonesian and an evaluation on using cross-lingual zero-shot question answering-based models to tackle clikcbait spoiling for low-resource language like Indonesian. We utilize selection of multilingual language models. The experimental results suggest that XLM-RoBERTa (large) model outperforms other models for phrase and passage spoilers, meanwhile, mDeBERTa (base) model outperforms other models for multipart spoilers.

翻译：点击诱饵揭穿旨在生成简短文本，以消除点击诱饵帖子引发的用户好奇心。由于这是一项新提出的任务，目前仅存在英语数据集。本文的贡献包括：构建了印尼语人工标注的点击诱饵揭穿语料库，并评估了基于跨语言零样本问答模型处理印尼语等低资源语言点击诱饵揭穿任务的效果。我们选取了多种多语言语言模型进行实验。结果表明，XLM-RoBERTa (large) 模型在短语型和段落型揭穿任务中表现最优，而 mDeBERTa (base) 模型在多部分型揭穿任务中优于其他模型。