Engaging in a live debate requires, among other things, the ability to
effectively rebut arguments claimed by your opponent. In particular, this
requires identifying these arguments. Here, we suggest doing so by
automatically mining claims from a corpus of news articles containing billions
of sentences, and searching for them in a given speech. This raises the
question of whether such claims indeed correspond to those made in spoken
speeches. To this end, we collected a large dataset of $400$ speeches in
English discussing $200$ controversial topics, mined claims for each topic, and
asked annotators to identify the mined claims mentioned in each speech. Results
show that in the vast majority of speeches debaters indeed make use of such
claims. In addition, we present several baselines for the automatic detection
of mined claims in speeches, forming the basis for future work. All collected
data is freely available for research.

本文提出使用自动挖掘新闻文章中的争议性观点，在辩论中寻找其对应的观点，并通过对英文辩论的 400 次演讲分析，证明了这种方法的可行性，并提供了基础的检测措施，所有数据均可供研究者免费使用。