What is Automatic Summarization? - Guide

There are two main approaches to automatic summarization. Extractive summarization selects the most important sentences from the original text and combines them into a summary. Abstractive summarization generates new sentences that paraphrase and condense the original content, similar to how a human would write a summary.

Extractive methods are simpler and tend to preserve the exact wording of the source. Abstractive methods, powered by large language models, produce more natural and concise summaries but carry a small risk of introducing inaccuracies. Most modern tools, including Notella, use abstractive summarization because it produces more readable output.

Automatic summarization is valuable in any situation involving large volumes of text: summarizing meeting transcripts, condensing research papers, creating briefings from news articles, or reducing long lecture recordings to study-friendly notes. The time savings can be substantial, turning a 30-minute recording into a one-page summary in seconds.