Medical Text Summarization

Medical text summarization aims to automatically condense lengthy medical records, notes, and conversations into concise, informative summaries, improving efficiency and accessibility of healthcare information. Current research focuses on developing abstractive summarization models, often leveraging large language models (LLMs) like GPT-4 and others, and employing techniques such as fine-tuning, contrastive learning, and vocabulary adaptation to enhance accuracy and faithfulness. A key challenge is ensuring summaries are both informative and factually consistent with the source material, leading to investigations into improved evaluation metrics and the incorporation of medical knowledge into model training. These advancements hold significant potential for streamlining clinical workflows, facilitating medical research, and improving patient care.