Generative summarization leverages the capabilities of large language models (LLMs) to create concise and coherent summaries from extensive text inputs, such as long-form chats, emails, reports, contracts, policies, and other documents. Unlike extractive summarization, which directly pulls key phrases or sentences from the text, generative summarization rephrases and condenses the original content into a shorter, yet comprehensive, version.
This technique uses the understanding of context and meaning provided by pre-trained language models to distill complex information, capturing the core ideas while maintaining accuracy and relevance. The output is a summary that is both informative and easily digestible, offering quick comprehension of the original content. Generative summarization is particularly useful in situations where fast processing and understanding of large volumes of text are required, making it ideal for business, legal, and research applications.