论文标题
点击诱饵通过问答回答和通过检索
Clickbait Spoiling via Question Answering and Passage Retrieval
论文作者
论文摘要
我们介绍并研究了点击诱饵破坏的任务：生成一个满足点击诱饵帖子引起的好奇心的简短文字。点击诱饵链接到网页，并通过引起好奇心而不是提供内容丰富的摘要来宣传其内容。我们的贡献是对所需的扰流板类型进行分类（即短语或段落）的方法，并产生适当的扰流板。对5,000个手动宠坏的点击订单的新语料库进行大规模评估和错误分析 - 网络签署宠坏了2022年 - 表明，我们的扰流板类型分类器的精确度为80％，而回答模型Deberta-large的问题却在两种类型的发电机上都胜过所有其他类型的扰流器。
We introduce and study the task of clickbait spoiling: generating a short text that satisfies the curiosity induced by a clickbait post. Clickbait links to a web page and advertises its contents by arousing curiosity instead of providing an informative summary. Our contributions are approaches to classify the type of spoiler needed (i.e., a phrase or a passage), and to generate appropriate spoilers. A large-scale evaluation and error analysis on a new corpus of 5,000 manually spoiled clickbait posts -- the Webis Clickbait Spoiling Corpus 2022 -- shows that our spoiler type classifier achieves an accuracy of 80%, while the question answering model DeBERTa-large outperforms all others in generating spoilers for both types.
