通过问答和段落检索的点击诱饵破坏
Clickbait Spoiling via Question Answering and Passage Retrieval
摘要：
我们介绍和研究的点击诱饵破坏的任务：生成一个简短的文本，满足好奇心引起的点击诱饵后。点击诱饵链接到一个网页，并通过引起好奇心而不是提供信息摘要来解释其内容。我们的贡献是分类所需扰流板类型的方法（即，短语或段落），并生成适当的剧透。一个大规模的评估和错误分析的一个新的语料库的5,000手动破坏clickbait职位-Webis Clickbait破坏语料库2022 -表明，我们的剧透类型分类器实现了80%的准确率，而问答模型DeberTa-large优于所有其他人在生成两种类型的剧透。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
