Text Summarization

Text Summarization is the natural-language-processing task of condensing a source document into a shorter version that preserves its key facts, sentiment, and intent. Modern systems use large language models (LLMs) to generate two styles: extractive , which selects the most important sentences verbatim, and abstractive , which rewrites content in new words, often with higher coherence but greater risk of fabrication. The workflow starts by tokenizing input, encoding it into vectors with a Transformer, and decoding a summary under length, coverage, or headline constraints. Fine-tuned models—GPT-4 Turbo, Gemini, Llama 3—support domain-specific guides like “TL;DR bullets,” “legal brief,” or “executive one-pager.” Evaluation combines ROUGE or BERTScore with LLM-based metrics for factuality and fluency. Summaries power news digests, meeting minutes, and chat inbox triage, and they serve as a grounding step in Retrieval-Augmented Generation (RAG) pipelines to lower context length and costs.

Related terms

• Amazon Titan Text
• Define text to speech
• LangChain Text Loader
• LangChain text splitter
• Speech-to-Text

Vstorm builds production systems that use Text Summarization: Agentic AI consulting.

← Back to the AI Glossary