Text Summarization — Extractive and Abstractive
Also known as: automatic summarization, extractive summarization, abstractive summarization, Otomatik Metin Özetleme
Automatic text summarization is a natural-language-processing task that condenses long documents into shorter summaries while preserving their key information. It works through one of two families of approaches — extractive summarization, which selects the most important spans from the source, or abstractive summarization, which generates new text. The field was consolidated by Nenkova and McKeown (2011), and sequence-to-sequence models such as BART (Lewis et al., 2020) advanced the abstractive side.
Key highlights
- Condenses long documents into shorter summaries while preserving the key information.
- Offers two complementary approaches: extractive selection of source spans and abstractive generation of new text.
- Scales the reading effort across many or lengthy documents that would be slow to read in full.
Intuition
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How it works
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When to use it
Text summarization fits when you have documents long enough to be worth condensing and the source language is supported by the chosen approach. It suits descriptive and exploratory goals over text data, working on cross-sectional or longitudinal collections. With too few documents an abstractive model cannot be trained reliably — fall back to keyword extraction for the key information — and with no text data at all summarization cannot run.
Strengths & limitations
- Condenses long documents into shorter summaries while preserving the key information.
- Offers two complementary approaches: extractive selection of source spans and abstractive generation of new text.
- Scales the reading effort across many or lengthy documents that would be slow to read in full.
- Abstractive models cannot be trained reliably on very few documents.
- Performance depends on the source language being supported by the chosen approach.
- Documents must be long enough to have meaningful content to compress.
Common pitfalls
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Applications
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Frequently asked
Extractive or abstractive — which should I use?
Extractive summarization selects and stitches together the most important spans directly from the source, so it stays faithful to the original wording. Abstractive summarization uses a sequence-to-sequence model to generate new, condensed text, which can read more naturally but requires a trained model and enough data. Extractive is the safer default on small or sensitive corpora; abstractive suits larger collections where natural rewriting matters.
How many documents do I need?
Summarization targets documents of sufficient length, with a practical minimum around 20. Training or relying on an abstractive model on fewer than roughly 100 documents is unreliable; in that case use keyword extraction to capture the key information instead.
Does it work in languages other than English?
Yes, provided the source language is supported by the chosen approach. Applying a model or extractive technique to an unsupported language is a common cause of poor summaries.
What if I have no text data?
Summarization cannot run without text. If your data is structured rather than free text, descriptive statistics are the appropriate alternative for conveying its key information.
Sources
- 1.Nenkova, A. & McKeown, K. (2011). Automatic Summarization. Foundations and Trends in Information Retrieval.
- 2.Lewis, M. et al. (2020). BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension. ACL.
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Cite this page
ScholarGate. (2026, June 1). Text Summarization. ScholarGate. https://scholargate.app/text-mining/text-summarization