Mitigating toxic content is critical for maintaining a healthy social platform, yet existing detoxification systems face significant limitations: overcorrection from uniformly processing all toxic comments, and parallel data scarcity in paraphrasing model training. To tackle these challenges, we propose Detoxifiability-Aware Detoxification (DID), a novel paradigm that adaptively conducts filtering or paraphrasing for each toxic comment based on its detoxifiability, namely whether it can be paraphrased into a benign comment in essence. Specifically, DID integrates three core modules: (1) an unsupervised detoxifiability discriminator, (2) a semantic purification module that extracts harmful intents and then performs targeted paraphrasing only on detoxifiable comments and (3) a feedback-adaptive refinement loop that processes remaining harmful contents only when they are detoxifiable. Experimental results demonstrate that DID significantly outperforms existing approaches on academic data and an industrial platform, establishing a novel and practical modeling paradigm for comment detoxification.