Productivity
Quick answer
Paste an article and the summarizer returns the key points, the argument in one sentence, and the reading time you saved. It also flags whether the piece is worth reading in full, which is the decision your saved-articles list actually needs help with.
Paste the article you keep meaning to read. Get the sentences that carry the argument, plus what you saved by skipping the rest.
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Key points
What the piece is about
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This is an extractive summary: it scores every sentence by how many of the article's most frequent meaningful words it contains, gives the opening lines a small bonus, then returns the top scorers in their original order so the argument still reads in sequence.
Extractive summaries never invent a claim the author did not make, which is their real advantage over generated prose. The trade-off is that a poorly structured article produces a poorly structured summary — garbage in, garbage out still applies.
Use three sentences when you only want to decide whether the piece is worth reading, and six to eight when you want notes you can keep instead of the original.
| What it answers | Key points and time saved from any article. |
|---|---|
| How the answer is produced | The summariser works extractively: it ranks the sentences already in the article rather than generating new prose. |
| What you need to enter | Paste the article body only; navigation text and comment sections distort the ranking. |
| Where it stops being reliable | Extractive summaries inherit the source's bias and cannot flag it. |
| Cost and sign-up | Free, runs in your browser, no account and no stored inputs. |
The summariser works extractively: it ranks the sentences already in the article rather than generating new prose. That choice trades a little elegance for a large gain in reliability, because an extractive summary cannot invent a claim the source never made.
Sentences are scored on term frequency against the whole document, position within their paragraph, and the presence of signal phrases such as findings, concluded, or resulted in. The highest-scoring sentences are returned in original document order so the argument still reads in sequence.
Reading-time savings are calculated from the word count of the source against the summary at an average adult reading speed, which gives you a quick sense of whether the full piece is worth your remaining attention.
A generated summary reads more smoothly than an extractive one because it is free to invent transitions and paraphrase awkward sentences into tidier ones. That freedom is exactly the risk: a model paraphrasing a claim can subtly shift its meaning, and there is no way to check without returning to the source anyway, which defeats the purpose of summarising at all.
Scoring sentences on term frequency, paragraph position and signal phrases is a deliberately old-fashioned technique, and its age is the point. It has no capacity to hallucinate a statistic or attribute a quote to the wrong person, because every sentence in the output already existed, verbatim, in the input. The trade-off is a summary that occasionally reads slightly disjointed where two high-scoring sentences were not adjacent in the original.
Reading-time savings are calculated honestly from word counts rather than estimated, which matters because the entire premise of a read-it-later queue is triage: deciding what earns your remaining attention today. A summary that overstates its own compression just relocates the problem it was meant to solve.
The honest use of a summarizer is triage rather than replacement. A summary is reliable for deciding whether an article is worth twenty minutes, and unreliable as the thing you cite, quote or act on, because it drops exactly the qualifications and caveats that make a claim safe to repeat. Used as a filter it clears a backlog that would otherwise sit unread for a year; used as a substitute it produces a confident second-hand understanding of an argument you have not actually encountered, which is worse than not having read it.
Term-frequency scoring has a specific blind spot worth knowing before you rely on it: a short article that makes one point well will score evenly across most of its sentences, while a long article that buries its actual conclusion in a final paragraph after several throat-clearing sections can have that conclusion under-ranked simply because the vocabulary supporting it appears fewer times than the vocabulary used for context-setting earlier on. When a summary of a long piece feels oddly inconclusive, that is often exactly what happened, and it is worth reading the final two paragraphs of the source directly.
Set the summary to roughly ten percent and the tool returns around six sentences pulled from across the piece, in original order, cutting reading time from roughly seven minutes to under a minute — enough to decide whether the full investigation is worth reading in full.
A 2,500-word technical explainer produces a denser fifteen-percent summary because each sentence carries more unique information; a ten-percent cut would have dropped a load-bearing paragraph explaining the core mechanism the rest of the article depends on.
Summarise all forty, then sort into three piles: read in full (usually four or five), keep the summary only, and delete. The pile that matters is the third — most saved articles were saved on a title and a moment of optimism, and seeing their actual claim in three sentences makes the decision to delete easy. The backlog stops being a source of guilt and becomes a short reading list you will actually finish.
Because every sentence you read is verifiably in the original. Generated summaries can introduce claims the author never wrote, which is unacceptable for reading you intend to rely on.
No. Ranking happens locally in your browser.
Around a tenth of the original for a news or blog piece; longer for dense technical material where each sentence carries more.
Because the highest-scoring sentences were not adjacent in the source article. The tool preserves their original order but does not add connecting language between them, which is a deliberate trade-off — it keeps every sentence verifiably from the source rather than inventing a smoother transition that risks changing the meaning.
Written and reviewed by Jim Vernon, Editor, AI Intelligence International. Published by AI Answer Engine, a service of AI Intelligence International, and checked against our editorial standards.
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