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Claim detection and verification are crucial for news understanding and haveemerged as promising technologies for mitigating misinformation anddisinformation in the news. However, most existing work has focused on claimsentence analysis while overlooking additional crucial attributes (e.g., theclaimer and the main object associated with the claim). In this work, wepresent NewsClaims, a new benchmark for attribute-aware claim detection in thenews domain. We extend the claim detection problem to include extraction ofadditional attributes related to each claim and release 889 claims annotatedover 143 news articles. NewsClaims aims to benchmark claim detection systems inemerging scenarios, comprising unseen topics with little or no training data.To this end, we see that zero-shot and prompt-based baselines show promisingperformance on this benchmark, while still considerably behind humanperformance.

NewsClaims: A New Benchmark for Claim Detection from News with Attribute Knowledge