> ## Documentation Index
> Fetch the complete documentation index at: https://docs.scaledown.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Overview

> Generate fluent, faithful summaries of long text.

## 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.** The `instructions` 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

| Use case            | Example                                                                |
| ------------------- | ---------------------------------------------------------------------- |
| Document review     | Summarize legal contracts, research papers, or policy documents        |
| News digests        | Condense articles to one-paragraph or bullet-point briefs              |
| Customer feedback   | Summarize support tickets, reviews, or survey responses at scale       |
| Meeting transcripts | Generate action-item-focused summaries from call recordings            |
| Financial reporting | Extract key figures and decisions from earnings calls or filings       |
| Content pipelines   | Pre-process long articles before passing them to downstream models     |
| RAG pre-processing  | Summarize retrieved chunks to reduce token overhead before model calls |

## 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.

```
[Ingest document] → [POST /summarization/abstractive] → [Summary] → [Store / display / pass to model]
```

The response includes `input_chars` and `output_chars` so you can track compression ratios across your pipeline, and `latency_ms` for performance monitoring.

## Abstractive vs. extractive summarization

|                | Extractive                              | Abstractive (this endpoint)               |
| -------------- | --------------------------------------- | ----------------------------------------- |
| How it works   | Selects and stitches existing sentences | Rewrites content in the model's own words |
| Output quality | Can feel disjointed                     | Fluent and coherent                       |
| Faithfulness   | Directly tied to source                 | Validated - rejects off-task outputs      |
| Flexibility    | Limited                                 | Controllable via `instructions`           |