Free with an account · 3 lengths in one run
Paste essays, transcripts, emails, or long articles — get a TL;DR, a standard summary, and a detailed version, plus key points and keywords. One run.
See how it works — click any example
100 to 50,000 characters per run. Three summary lengths produced in one pass — TL;DR (20-55 words) / standard (80-160 words) / detailed (180-320 words). Six focus modes (general / academic / news / business / technical / key-facts) and three audience levels tune what gets emphasized.
Headline, TL;DR, standard paragraph, detailed summary, 5-10 key points, 5-12 keywords, named entities, sentiment, and tone — all from one credit charge. Convert to a mind map or flashcards, or pull from a URL with the article summarizer.
Under 100 characters — too little to summarize. Over 50,000 characters — split into chunks, or use the PDF summarizer for longer documents. Will never invent facts or neutralize opinionated source text.
Free with an account — 30 runs/month. Upgrade to Pro for 1,500/month.
Input: 470-word study on remote-first engineering teams · English output, trimmed.
Headline
Remote-first teams ship more individually but erode mentorship, collaboration, and connection unless deliberately protected.
TL;DR
A 1,200-engineer survey found remote-first work raises individual output 11% but reduces mentorship by a third and cross-team collaboration by 22%. Companies that fund retreats and structured mentorship reverse the trend.
Standard summary
Across 47 companies, remote-only engineers produced 11% more individual output than hybrid peers but reported a 33% drop in mentorship and a 22% drop in cross-team pull requests. Forty-one percent felt isolated most days. The companies that avoided this trade-off did not mandate return-to-office — they funded in-person retreats, paired new hires with tenured mentors for 90 days, and protected one meeting-free day per week. These companies saw 18% higher retention with no drop in individual output. The takeaway: remote is not a neutral default; second-order effects like mentorship and cross-pollination must be actively protected as engineering investments, not HR soft skills.
Key points
Keywords
Detected
Content type: research / survey · Language: English · Sentiment: mixed (positive on output, negative on connection) · Reading level: college · Source: 470 words · Compression: 53% (standard), 17% (TL;DR)
Try it with any text · Results usually in 10-30 seconds · Copy as markdown or share via link
You paste any block of text (100-50,000 characters) and we return six things in a single run: a headline, a TL;DR (1-2 sentences, 20-55 words), a standard summary (80-160 word paragraph), a detailed summary (180-320 words), 5-10 key points, 4-8 short skim bullets, 5-12 keywords, named entities mentioned in the text, overall sentiment, tone, and the source reading level. You get all three length tiers from one API call — no rerunning to get a different length.
The Article Summarizer takes a URL and fetches the page for you. The PDF Summarizer takes a file upload. This one takes raw pasted text — any source, any format. Use it for transcripts, emails, pasted book chapters, meeting notes, anything where you already have the text and just want a summary.
No. The prompt explicitly forbids adding facts, numbers, names, or context that are not in the source. If the source hedges ("may", "could"), the summary preserves the hedge. If the source argues for a position, the summary conveys that it argues for it — it does not neutralize into "the text discusses both sides" unless the text genuinely does.
We strip about 30 tired phrases that signal AI filler — "In conclusion", "In summary", "The article discusses", "In today's fast-paced world", "delve into", "leverage", "paradigm shift", "game-changing", and similar. A summary should read like a sharp editor wrote it, not an overcaffeinated B2B blog.
Yes. Six focus modes — general, academic, news, business, technical, key-facts — tune what the model emphasizes. Three audience levels (general / beginner / expert) tune how much jargon is defined versus preserved. The combination handles everything from "summarize this research paper for my PhD committee" to "explain this SEC filing like I have never seen one".
Minimum 100 characters (about 20 words). Maximum 50,000 characters (roughly 8,000-10,000 words, or a full research paper chapter). Larger texts are truncated. For longer content, paste in chunks or use the PDF Summarizer.
Any language the model reads — English, Chinese, Japanese, Korean, Spanish, French, German, Russian, Portuguese, Italian, and dozens more. The summary is returned in the same language as the source by default, or you can set a target language in the request. The English UI comes first; other UI translations roll out next.
Set focus to "general" or "news" and we collapse verbal filler (um, uh, you know) and repetition automatically. We flag in the output that the source was a transcript. Speaker attributions are preserved if the transcript has them.
Yes. We count the words in each summary tier server-side and show the true numbers on the result page, including compression ratio versus the source. LLMs are known to undercount their own output by 10-30% — we never trust the model's self-report.
The first 3 summaries per browser per day are free with no signup. Sign up for 30 summaries per month free, or go Pro for 1,500 per month plus history, export, and no rate limits.
Because skimming is layered. You look at the TL;DR to decide whether to read further. If yes, you scan the standard summary to triage. If you need detail, you read the detailed summary. Getting all three in one pass means one wait, one credit charge, one share link — with the length choice made at the moment of reading, not at the moment of generation.