Extractive summarization is a technique in natural language processing that involves selecting and compiling key sentences or phrases from a larger text to create a concise summary. This method focuses on identifying the most important information without altering the original wording, making it easier for readers to grasp the main ideas quickly. The process typically uses algorithms to analyze the text and determine which parts are most relevant based on factors like frequency of terms and sentence structure. Machine learning models and text ranking methods are often employed to enhance the accuracy of the summaries generated.