Summarization
Definition
Automatic summarization condenses texts to central information by selection or rephrasing.
Automatic summarization condenses one text or multiple documents to central information, extractively by selection or abstractively by rephrasing.
Simply explained
From a long text, a shorter version is created. The system takes over important passages or reformulates their core statements.
Why it matters in practice
The term helps to select, develop, evaluate AI systems in a well-founded manner and realistically assess their technical risks.
Example from business
An internal system creates a draft from long protocols and links each statement to the source location.
Limits and pitfalls
- Abstract summaries can change or omit facts; length, target audience, and source verification must be specified.
Origin & history
Research ranges from statistical sentence extraction in the 1950s to neural encoder-decoder and language models.
Use in mid-sized companies
- Possible application: An internal system creates a draft from long protocols and links each statement to the source location.
Frequently asked questions
What does summarization mean?
Automatic summarization condenses one text or multiple documents to central information, extractively by selection or abstractively by rephrasing. What should you watch out for? Abstract summaries can change or omit facts; length, target audience, and source verification must be specified.
Sources
- Automatic Summarization · Nenkova and McKeown, Automatic Summarization
Source checked on 10.10.2026. Editorial application examples are hypothetical; no proof of real customer results.
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