Speech Summarization

Speech summarization aims to automatically generate concise text summaries from spoken audio, addressing the need for efficient processing of increasingly prevalent audio data. Current research focuses on end-to-end models, often leveraging large language models (LLMs) and incorporating techniques like knowledge distillation and block-wise processing to handle long audio inputs and improve summarization accuracy. These advancements are significant for applications ranging from automated meeting transcription to medical record analysis, improving accessibility and efficiency in various domains.