NewsClaims: A New Benchmark for Claim Detection from News with Attribute Knowledge
This addresses the need for more comprehensive claim detection in news to combat misinformation, though it is incremental by extending existing work with additional attributes.
The authors tackled the problem of claim detection in news by introducing NewsClaims, a benchmark that includes attribute extraction beyond just sentences, resulting in a dataset of 889 claims annotated over 143 articles.
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.