  Claim detection and verification are crucial for news understanding and have
emerged as promising technologies for mitigating misinformation and
disinformation in the news. However, most existing work has focused on claim
sentence analysis while overlooking additional crucial attributes (e.g., the
claimer and the main object associated with the claim). In this work, we
present NewsClaims, a new benchmark for attribute-aware claim detection in the
news domain. We extend the claim detection problem to include extraction of
additional attributes related to each claim and release 889 claims annotated
over 143 news articles. NewsClaims aims to benchmark claim detection systems in
emerging scenarios, comprising unseen topics with little or no training data.
To this end, we see that zero-shot and prompt-based baselines show promising
performance on this benchmark, while still considerably behind human
performance.
