The increasing volume of radiology reports presents a critical need for automated, accurate summarization tools to support clinical efficiency and diagnostic clarity. This thesis proposes a hybrid deep learning framework for summarizing radiology reports, specifically tailored for the chest X-ray domain using the Indiana University Chest X-ray Collection. The objective is to generate fluent, accurate, and clinically meaningful impression-style summaries that align with radiologists’ diagnostic language, thereby supporting efficient clinical decision-making and documentation. The pipeline is composed of three sequential stages. First, an extractive summarization step selects key sentences from the findings section using BERT-based sentence embeddings and cosine similarity ranking.