Text Summarization: Definition and Examples

Text summarization is one of the oldest and most useful applications of natural language processing (NLP). It transforms a lengthy text — article, report, book, transcript — into a concise synthesis that captures key points without losing the fundamental meaning of the source document.

There are two main approaches to automatic summarization. The **extractive** method selects and assembles the most important sentences from the original text without rewording them. The **abstractive** method, made possible by large language models (LLMs), generates new sentences that rephrase and condense the information, producing more natural and fluent summaries closer to what a human would write.

With the emergence of LLMs like Claude, GPT, or Gemini, text summarization has seen a major qualitative leap. These models can understand context, prioritize information, and produce summaries tailored to different audiences or formats. In prompt engineering, the quality of the summary depends heavily on how the request is formulated: desired length, level of detail, tone, target audience.

Text summarization is now ubiquitous in the professional world: meeting summaries, information monitoring, legal document summarization, scientific article condensation, and executive brief creation. Mastering prompting techniques for summarization has become a key skill for anyone working with AI daily.