Large Language Models (LLMs) have advanced machine translation, yet long-text translation still faces a practical tension between document-level lexical consistency and inference cost. Recent document-level methods improve coherence by expanding context, maintaining multi-level memories, or injecting multi-granularity knowledge, but such broad context mechanisms often introduce substantial prompt redundancy and limit parallel inference. We propose NoBAT (Noun Bank Augmented Translation), a lightweight framework that distills document-level lexical constraints into a compact global Noun Bank. NoBAT first constructs and disambiguates the Noun Bank serially, and then retrieves only relevant noun constraints for local, parallel translation. Experiments on IWSLT2017 and Novels-XL with multiple LLM backbones show that NoBAT consistently improves lexical translation consistency while maintaining competitive COMET scores. Compared with DELTA, NoBAT further reduces total token consumption by 6.3×, demonstrating a cost-effective trade-off among consistency, translation quality, and efficiency.