Summarize for LLM
AI / AgentExtractive summarization: pick the most informative sentences to fit within a token budget for LLM context windows.
Paste a long document and a token budget and get back the most informative sentences, kept verbatim and in their original order, which is a fast way to compress background material before it eats into a model's context window. Sentences are scored by term-frequency density and position (earlier sentences get a slight bonus) and greedily selected until the budget runs out, so this is extractive summarization, not rewriting: nothing is paraphrased, and no LLM call is involved. Because informativeness here is statistical rather than semantic, an important but rarely-repeated sentence can get passed over in favor of a more frequent but less critical one, so it's worth spot-checking the output against the source for anything high-stakes. Token counts are estimated from character length rather than a real tokenizer, which is close enough for budgeting but not exact.
How to use Summarize for LLM
- 1.Paste a long document and set a target token or sentence budget to get back the most informative sentences, extracted rather than rewritten.
- 2.Use it to compress background material before pasting it into a chat with limited context window, preserving the source wording exactly.
- 3.Compare the extractive summary against the full text to quickly judge whether key details were preserved before trusting it as LLM context.
Frequently asked questions
Use via API, SDK, or MCP
cURL# Free: 1,000 req/day · Pro: 10,000 req/day
curl -X POST https://api.utilix.tech/v1/tools/summarize-for-llm \
-H "Authorization: Bearer utx_live_..." \
-H "Content-Type: application/json" \
-d '{"text":"The quick brown fox jumps over the lazy dog. This sentence is often used to test typefaces because it contains every letter of the alphabet.","maxTokens":200,"strategy":"extractive"}'Get an API key from your dashboard · Full API docs →