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Glossary

Text Summarisation

What is 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.

How KI.ordo helps

The KI.ordo template “Summarise text” produces short, structured summaries without any set-up of your own.

Learn more →: Text Summarisation

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Topics

  • NLP
  • Summarisation
  • Productivity

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Related terms

ROUGE score

ROUGE (Recall-Oriented Understudy for Gisting Evaluation) is a family of metrics for comparing automatically generated texts – especially summaries – with reference texts written by people. It measures how many words or word sequences overlap, for example single words in ROUGE-1 or the longest common subsequence in ROUGE-L. The score is quick to compute, but it does not reliably capture paraphrases with the same meaning or factual accuracy.

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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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Template

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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