AI Summarization: Definition and Examples

AI Summarization, or automatic summarization by artificial intelligence, is a natural language processing (NLP) technique that allows an AI model to produce a condensed version of a document while preserving the main ideas and overall meaning. This capability has become one of the most common and useful applications of large language models (LLMs).

There are two main approaches. Extractive summarization selects and assembles the most important sentences or passages from the original text without modifying them. Abstractive summarization, more advanced, generates new sentences that rephrase and synthesize the content, producing a more natural and fluent summary similar to what a human would write.

Modern models like Claude, GPT, or Gemini excel at abstractive summarization thanks to their ability to understand context, identify information hierarchy, and rephrase accurately. They can summarize articles, reports, meeting transcripts, discussion threads, or even entire books.

In prompt engineering, mastering AI Summarization means knowing how to guide the model to obtain summaries tailored to your needs: target length, level of detail, specific angle, target audience, or output format. A good summary prompt specifies these constraints to get a immediately usable result.