This paper describes our system for the L-SUMM shared task on legal document summarization. Our approach is built on the Longformer Encoder-Decoder (LED) model, which we augment with a multi-level summarization strategy tailored for legal documents that are substantially longer than typical transformer input limits. The system achieved competitive performance on the legal judgment summarization task through optimized training strategies, including gradient accumulation, Adafactor optimization, and hyperparameter tuning. Our findings indicate that combining hierarchical processing with strategically assigned global attention enables more reliable summarization of lengthy legal texts.