What is Extractive Summarization?
TL;DR
A summarization approach that selects and pulls the most important existing sentences from a source, rather than rewriting them.
Extractive Summarization: Definition & Explanation
Extractive summarization is an approach that identifies and pulls the most important existing sentences or phrases directly from a source document to form a summary, without rewriting them. It contrasts with abstractive summarization, where a large language model (LLM) generates new sentences that paraphrase the gist. Because extractive methods reuse the original wording, they carry a lower risk of hallucination—the summary cannot easily add facts the source never stated—but the result can read choppily and may still drop key context. Most modern consumer tools are abstractive or hybrid, so they read more naturally yet require checking against the original. Tools like TLDR This condense web articles into key sentences or bullets, and Scholarcy extracts findings, methods, and references from academic papers. Cautions: even extractive summaries omit detail and can miss the point that mattered, long documents may be only partially processed, and confidential files may be sent to a third-party cloud—verify the digest against the source and review data policies.