Extractive summarization is a technique in natural language processing (NLP) that identifies key pieces of information from a text and selects the most relevant segments or fragments to form a concise summary. Unlike abstractive summarization, which generates new sentences, extractive summarization simply extracts portions of the original text, such as sentences or phrases, and arranges them to highlight the main points.
This method is typically used for summarizing large volumes of content quickly, such as articles, reports, or documents, by selecting only the most important and contextually relevant information.