NLP-Based Clinical Text Summarization Using Transformers

Abstract

The increasing volume of clinical text data, including electronic health records, discharge summaries, and medical reports, has created a need for efficient summarization techniques to support clinical decision-making. This paper presents an NLP-Based Clinical Text Summarization framework using Transformer-based architectures to generate concise and meaningful summaries from unstructured medical text. The proposed system leverages advanced models such as Bidirectional Encoder Representations from Transformers to capture contextual and semantic relationships within clinical narratives. Both extractive and abstractive summarization approaches are explored to enhance information retention and readability. The framework incorporates domain-specific preprocessing and fine-tuning to improve performance on medical datasets. Experimental results demonstrate that the proposed approach achieves high-quality summaries with improved coherence and relevance compared to traditional methods. Additionally, integration with prior research in machine learning and healthcare analytics enhances system robustness and adaptability. The study highlights the potential of transformer-based NLP models in reducing clinician workload and improving the efficiency of healthcare information management.