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This paper proposes a parameter-efficient vocabulary adaptation method for LLM-based text summarization in specialized domains, augmenting pretrained tokenizers with domain-specific tokens and selectively replacing under-trained ones to reduce training time by 35-55% and parameter counts by up to 37%.