This paper addresses the challenge of improving document-level machine translation using Large Language Models. The authors propose a two-stage fine-tuning strategy that augments training data by converting summarization data into document-level parallel data using LLMs. To ensure data quality, they filter the synthetic corpus using multiple metrics including sacreBLEU, COMET, and LaBSE-based cosine similarity. The approach tackles two key challenges: the scarcity of large-scale document-level parallel data and the tendency of LLMs to generate hallucinations and omissions. By leveraging LLMs' strength in modeling contextual information, this method aims to improve coherence across sentences in translation tasks.