# Towards Effective Rebuttal: Listening Comprehension using Corpus-Wide Claim Mining

## TL;DR

This paper explores automatic claim mining from large news corpora to identify arguments in speeches, demonstrating that mined claims are frequently used in debates and providing baselines for claim detection.

## Contribution

It introduces a method for mining claims from extensive news datasets and validates their relevance in speeches, offering a new resource and baseline for rebuttal research.

## Key findings

- Most speeches contain mined claims relevant to the debate topics.
- Large-scale claim mining can effectively identify arguments in spoken debates.
- The dataset and baselines are publicly available for further research.

## Abstract

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.