  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.
