Conversation Summarization

Conversation summarization, particularly in the medical domain, aims to automatically generate concise and accurate summaries of doctor-patient dialogues, improving efficiency and information access. Current research focuses on developing robust models, often leveraging large language models (LLMs) and incorporating multi-modal data (text and visuals) along with external medical knowledge, to enhance summary accuracy and completeness. These advancements are crucial for streamlining healthcare workflows, facilitating better communication among medical professionals, and ultimately improving patient care. Challenges remain in handling out-of-domain data and optimizing model performance with limited training data.