Dialog Summarization

Dialog summarization aims to automatically generate concise and informative summaries of multi-turn conversations, focusing on accuracy and coherence. Current research emphasizes overcoming challenges like capturing speaker roles, ensuring factual consistency, and handling varying dialogue lengths and structures, often employing transformer-based encoder-decoder models and large language models (LLMs) for abstractive summarization. This field is crucial for managing large volumes of conversational data across various domains, such as customer service and meeting transcription, improving efficiency and facilitating better information retrieval. Ongoing work explores incorporating human feedback and leveraging semi-supervised learning techniques to enhance summarization quality.