Summarization is a technique used in [[natural language processing]] to extract the most relevant information from a text and present it in a condensed form. Summarization can be done manually, but it is often automated using algorithms and techniques that analyze the text and extract the most important information. This can save time and effort, especially when dealing with large amounts of text, and can help ensure the accuracy and completeness of the summary. Summarization is used in many fields, including journalism, research, and business, to help people quickly understand the main points of a text without having to read it in its entirety. Summarization is one of the main uses for [[instruction-tuned LLM|instruction-tuned LLMs]] that can be asked to provide a summary of a given text with a focus on specific topics. See examples of summarization for different purposes in [[product review summarization]]. [[provide more context]] < [[Hands-on LLMs]]/[[5 Prompting]] > [[increased specificity]]