Text Summarization - Condensing Documents Without Losing the Point
Text summarization produces a shorter version of a document while preserving the information a reader needs. It can extract existing sentences or generate new wording.
Extractive and Abstractive Methods
Extractive summarization ranks and selects source sentences. It is easier to trace to the source but can be repetitive or awkward.
Abstractive summarization generates a new summary with an encoder-decoder model or LLM. It can synthesize material across a document but may introduce unsupported claims.
source -> select or encode key content -> concise summaryDefining the Job
“Summarize this” is underspecified. Good requirements state the intended reader, length, format, and what must be retained:
- executive briefing with decisions and risks
- clinical handoff with medications and uncertainty
- meeting notes with owners and deadlines
- customer-facing plain-language explanation
Evaluation
ROUGE measures overlap with reference summaries, but high overlap does not prove a summary is complete or factual. Evaluate:
| Dimension | Question |
|---|---|
| Faithfulness | Is every claim supported by the source? |
| Coverage | Are the important points present? |
| Relevance | Does it fit the requested audience and purpose? |
| Concision | Is unnecessary detail removed? |
Human review and claim-level checks are especially important for long documents and high-impact content.
Safer Generation
Provide the source or retrieved citations in the prompt, require the model to distinguish facts from uncertainty, and ask it to say when the source does not answer a question. For long inputs, use hierarchical summaries and retain links to the original sections. A fluent summary is not necessarily a faithful one.