Abstractive vs extractive: why this matters for your results
There are two fundamentally different ways to shorten text, and knowing which you’re using sets the right expectations:
| Extractive | Abstractive (this tool) | |
|---|---|---|
| Method | Picks the most important existing sentences | Writes new, shorter sentences |
| Reads like | A highlighter pass | A human paraphrase |
| Faithfulness | High — every word is from the source | High but not perfect — can rephrase imperfectly |
| Risk | Choppy, disconnected | Rare hallucinated detail |
This tool uses abstractive summarization (a DistilBART model), which is why the output reads smoothly rather than like disconnected highlights. The trade-off is the small hallucination risk above — a fair price for summaries that actually sound written rather than stitched together.
Everything runs on your machine, which has consequences
Because the ~230MB model executes locally in a Web Worker, two things follow. First, privacy is absolute — your text never leaves the browser, so it’s safe for confidential drafts, contracts, and proprietary research. Second, speed depends on your hardware — a fast laptop summarizes in a few seconds; an older device or a phone takes longer, and the first run also has to download the model. This is the deliberate trade: no servers, no uploads, no per-request cost, in exchange for using your own compute.
Getting cleaner summaries
- Feed it clean prose. Strip navigation text, ads, and reference lists before summarizing — the model treats everything as signal and will dutifully summarize the noise.
- Chunk long documents. Summarize each major section, then combine. This avoids the mid-document context loss that hurts single-pass runs on long inputs.
- Mind the output limit. Transformer models cap their output length, so a summary of a very long text can end abruptly. If it cuts off mid-thought, your input was too long for one pass — split it.
When to reach for something else
A summarizer compresses; it doesn’t analyze, fact-check, or answer questions. If you need a specific answer buried in a document, the AI Question Answering tool extracts it directly. If you need to pull out names and places, the AI Entity Extractor is the right instrument. Use the summarizer for the “what is this about, quickly?” job it does best.