What it does
The/summarization/abstractive endpoint condenses text into a shorter, fluent rewrite in the model’s own words. Unlike extractive summarization - which lifts sentences directly from the source - abstractive summarization produces a coherent output that captures the key information without being constrained to the original phrasing.
When to use it
You need to condense long documents for humans or downstream models. Summaries reduce reading time for human reviewers and reduce token costs when passing document content to another model. You need format or focus control. Theinstructions field lets you specify bullet points, a particular language, a word limit, or a topical focus - without overriding the core faithful-summary behaviour.
You want consistent, low-noise outputs. Sampling parameters (temperature, top-p) are fixed to a configuration tuned for faithful summarization. You get stable, predictable outputs rather than creative or hallucinated ones.
Common use cases
How it fits into your workflow
Summarize works as a standalone step that can sit anywhere documents are consumed - before storing, before displaying, or before passing to another model.input_chars and output_chars so you can track compression ratios across your pipeline, and latency_ms for performance monitoring.