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With the rise of social media, users are exposed to many misleading claims.However, the pervasive noise inherent in these posts presents a challenge inidentifying precise and prominent claims that require verification. Extractingthe important claims from such posts is arduous and time-consuming, yet it isan underexplored problem. Here, we aim to bridge this gap. We introduce a noveltask, Claim Normalization (aka ClaimNorm), which aims to decompose complex andnoisy social media posts into more straightforward and understandable forms,termed normalized claims. We propose CACN, a pioneering approach that leverageschain-of-thought and claim check-worthiness estimation, mimicking humanreasoning processes, to comprehend intricate claims. Moreover, we capitalize onthe in-context learning capabilities of large language models to provideguidance and to improve claim normalization. To evaluate the effectiveness ofour proposed model, we meticulously compile a comprehensive real-world dataset,CLAN, comprising more than 6k instances of social media posts alongside theirrespective normalized claims. Our experiments demonstrate that CACN outperformsseveral baselines across various evaluation measures. Finally, our rigorouserror analysis validates CACN's capabilities and pitfalls.

From Chaos to Clarity: Claim Normalization to Empower Fact-Checking