What is Natural Language Generation?
Natural Language Generation enables computers to communicate findings or decisions in fluent, human-readable language. While Natural Language Processing (NLP) interprets text, NLG creates it - bridging the gap between machine data and human communication.
Modern NLG relies on machine learning and Large Language Models (LLMs) to generate text dynamically. Early rule-based NLG systems used templates (“If revenue > budget, say ‘profit increased’ ”); today’s transformer-based models can compose nuanced summaries, reports, or creative writing without explicit templates. NLG is central to Generative AI, enabling tools that can write, explain, or personalize at scale.
It’s the “output” side of language technology - the process that turns numbers, facts, or insights into readable sentences, summaries, or narratives. NLG powers automated report writing, conversational assistants, chatbots, and generative content systems.
How NLG Works
- Data Preparation – The system collects and structures input data (e.g., numbers, logs, transcripts).
- Content Determination – Chooses what information should appear in the output.
- Sentence Planning – Organizes content into logical order and determines phrasing.
- Surface Realization – Converts the plan into natural-sounding text.
- Post-Processing / Feedback Loop – Evaluates clarity, tone, and accuracy, often with human review.
In modern transformer models, these steps occur implicitly within neural layers trained to model both semantics and syntax.
