Natural language generation (NLG) is a field of artificial intelligence and computational linguistics that turns structured, usually non-linguistic input—such as numbers, data or semantic representations—into text people can read.
In practical terms, NLG can turn sales figures into a written report, forecast data into a spoken weather update, or a customer’s request into a chatbot response. Zapier describes it as the production side of language systems: structured meaning goes in, and fluent sentences come out.
NLG vs. NLP and NLU
Natural language processing (NLP) is the broad field covering how software works with human language, including tasks such as tokenization, translation and classification.
Natural language understanding (NLU) focuses on interpreting human input. It can identify intent, meaning and entities in a message. NLG performs the reverse operation by expressing structured information in natural language. A conversational system may use NLU to understand a customer’s shipping complaint and NLG to compose a response.
How NLG works
Traditional NLG systems are often described as a sequence of tasks: selecting relevant content, organizing it, combining related facts, choosing words, referring to entities and applying grammar and punctuation. Zapier notes that modern models generally combine these functions into a learned process rather than exposing them as separate stages.
Extractive and abstractive summaries
Extractive summarization selects important sentences from an existing document and reproduces them. This can keep the output close to the source, but the result may be choppy.
Abstractive summarization creates new wording after processing the source. It can read more naturally, but rewriting and recombining details can also introduce inaccuracies, including the AI hallucinations noted by Zapier.
Common NLG methods
- Templates: Prewritten sentences with fields populated by data, such as a mail merge.
- Rule-based systems: Explicit rules determine what to include, how to order it and which words to use.
- Statistical models: The system predicts likely next words from patterns in large collections of text.
- Deep learning: Neural networks learn language patterns from examples.
- Transformers and large language models: These use self-attention to weigh relationships across text and are behind many current language-generation systems.
Where NLG is used
NLG is useful wherever large amounts of data need to be explained or communicated. Examples include automated reporting, business-intelligence dashboards, personalized outreach, product descriptions, voice assistants, chatbots and AI agents.
Generating a response is not the same as taking an action. For example, an agent may draft a refund confirmation, but it needs a connection to a billing system to issue the refund, according to Zapier.
Practical takeaway
The method behind an NLG tool helps set expectations. Templates and rule-based systems are predictable, while statistical and language-model-based systems can produce more varied output. More natural wording does not eliminate the need to check generated text for accuracy, particularly when a system summarizes or recombines information.
Source
This report is based on information published by Zapier.
