What is extractive summarization?
Extractive summarization builds a summary by selecting the most important sentences from the source and using them as written. It cannot invent anything, because every sentence in the summary was in the original.
Extractive text summarization is spelled summarisation in British English; the method is the same either way.
How extractive summarization works
The text is split into sentences, each sentence is scored, and the highest-scoring ones are kept, usually in their original order. Scores come from signals such as how many distinctive terms a sentence carries (TF-ISF is one such measure), where it sits in the document and how much it overlaps with the rest of the text.
An extractive summarization example
From a 900-word article about a pricing change, an extractive summary might keep three sentences: the one announcing the change, the one giving the new price and the one explaining who is affected. Each is quoted exactly, so the figures are guaranteed to match the source.
Extractive vs abstractive summarization
Abstractive summarization writes new sentences that paraphrase the source. It reads more smoothly and can compress further, but it can also introduce claims, numbers or a tone the source never had. Extractive output needs light editing to flow, and in exchange it is always faithful to what was said.
Where extractive summarization fits
It suits any job where accuracy matters more than polish: quotes, key points, and summaries of interviews or technical text. AI Powered Content Repurposing is extractive by design: sentences come from your source rather than being generated, which keeps claims and figures accurate.
Last reviewed 2026-11-01.