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Natural language generation (NLG)

What is Natural language generation (NLG)?

Natural language generation (NLG) is the area of language processing that produces understandable text from data, bullet points or other inputs. Earlier NLG systems relied on templates and rules; today most use large language models. Typical uses include weather and stock market reports, product descriptions, summaries of key figures and chatbot answers. It is important that figures and facts from the inputs are carried over correctly and unchanged.

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KI.ordo’s “Report from bullet points” template turns notes or figures into a readable report and keeps the figures unchanged.

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  • language processing
  • text generation
  • reports

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Text Generation

Text generation is the automatic production of text by AI, nowadays usually by large language models that calculate the most likely continuation word by word. It is a branch of natural language generation and a core application of generative AI. Organisations use it to draft emails, reports or product descriptions; facts and figures should be checked before publication.

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Natural Language Processing (NLP)

Natural language processing is the branch of AI that enables computers to analyse, understand and generate human language in text or speech. It covers tasks such as translation, classification, summarisation, sentiment analysis and question answering. Rules and statistical methods once dominated; today the field is shaped by large language models.

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Natural Language Understanding (NLU)

Natural language understanding is the part of language processing that captures the meaning of an utterance: what intent lies behind it, which people, places or dates are mentioned, and what is its tone? Together with natural language generation (NLG), NLU forms one of the two sides of NLP. One example is a customer service system that recognises “My parcel still hasn't arrived” as a delivery enquiry.

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Text Summarisation

In automatic text summarisation, AI condenses long texts to their key points. Extractive methods copy important sentences verbatim, while abstractive methods rephrase the content; large language models work mainly abstractively. This is useful for minutes, reports or long email threads; for important decisions the summary should be checked against the original.

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