The paper presents a modular, two‐track lay‐summary generation system for biomedical research articles, evaluated on the PLOS and eLife subsets of the BioLaySumm2025 shared task. In Task 1, it extracts salient sentences via an LLM–based chunking and summarization pipeline, then applies iterative rewriting to produce an accessible summary. In Task 2, it augments that summary with UMLS‐sourced definitions identified by a BioBERT NER model, yielding improved readability and factual consistency, at the cost of slight reductions in n‐gram overlap metrics like ROUGE and BLEU.