  An educated and informed consumption of media content has become a challenge
in modern times. With the shift from traditional news outlets to social media
and similar venues, a major concern is that readers are becoming encapsulated
in "echo chambers" and may fall prey to fake news and disinformation, lacking
easy access to dissenting views. We suggest a novel task aiming to alleviate
some of these concerns -- that of detecting articles that most effectively
counter the arguments -- and not just the stance -- made in a given text. We
study this problem in the context of debate speeches. Given such a speech, we
aim to identify, from among a set of speeches on the same topic and with an
opposing stance, the ones that directly counter it. We provide a large dataset
of 3,685 such speeches (in English), annotated for this relation, which
hopefully would be of general interest to the NLP community. We explore several
algorithms addressing this task, and while some are successful, all fall short
of expert human performance, suggesting room for further research. All data
collected during this work is freely available for research.
