Summarise long text with a model running on your machine

Paste an article, a transcript or a report and get an abstractive summary from distilbart-cnn — a distilled BART fine-tuned on CNN/DailyMail that produces fluent multi-sentence summaries rather than extracted keywords. It runs on your GPU, so there is no length limit imposed by an API tier and no confidentiality question about pasting internal documents. Long inputs are chunked and summarised section by section.

free text summarizerai summarizer no signupdistilbart demoarticle summarizer offlineprivate document summarizerabstractive summarization onlinesummarize transcript locally

Frequently asked questions

How long can the input be?

The model itself reads about 1,024 tokens at a time, so longer text is split into chunks and each chunk summarised. There is no overall cap beyond your patience and your GPU.

Is it really free, and is there a usage limit?

It is free and there is no per-request limit, because we are not paying for the compute — your own GPU is. The only cost is the one-time model download, which your browser caches.

Models available for this task

DistilBART CNN 6-6 — Xenova/distilbart-cnn-6-6, 287 MB, 230M params
BART Large CNN — Xenova/bart-large-cnn, 466 MB, 406M params
DistilBART CNN 12-6 — Xenova/distilbart-cnn-12-6, 364 MB, 306M params
DistilBART XSum 6-6 — Xenova/distilbart-xsum-6-6, 287 MB, 230M params
DistilBART XSum 12-6 — Xenova/distilbart-xsum-12-6, 364 MB, 306M params
DistilBART CNN 12-3 — Xenova/distilbart-cnn-12-3, 312 MB, 255M params

Explore the rest of the playground

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LLM chat — Chat with a local LLM running on your own GPU
Text embeddings — Generate text embeddings locally, in the browser
Translation — Translate text offline with a local neural MT model
Sentiment analysis — Sentiment analysis that runs in your browser
Zero-shot text classification — Classify text into labels you make up on the spot
Named entity recognition — Named entity recognition, running on your own machine
Question answering — Ask questions about a passage, get the exact answer span
Fill-mask — Watch a masked language model guess the missing word

Guides & comparisons

Model catalog — The model catalog: what actually runs in a browser
WebGPU vs WASM — WebGPU vs WebAssembly for running models in a browser
Browser AI vs cloud API — Browser inference vs a hosted API: an honest comparison
Transformers.js vs WebLLM — Transformers.js vs WebLLM
Whisper model sizes — Choosing a Whisper size for browser transcription
fp32 vs q8 vs q4 — Picking a quantization level for browser inference
Why no image generation — Why this playground has no text-to-image generation
FAQ — Frequently asked questions about browser-based AI
About — About this project

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