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Extracted Keywords
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0/500 words used today
Paste any article, report, or essay. Get the 8–15 most important terms and phrases pulled out instantly — no login, no setup.
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Keyword extraction is one of those tasks that sounds basic until you realise how many workflows it unlocks. In a few seconds, it answers the question: "What is this text actually about?" — which turns out to be useful in a surprising number of situations.
For writers and content teams, keywords reveal whether a piece covers its topic thoroughly or drifts. For researchers, they map the conceptual territory of a paper without reading the full text. For students, they generate the vocabulary that should appear on flashcards or in a study guide.
Clearwrite extracts terms by semantic weight — it identifies what the text is fundamentally about, not just what words appear most frequently. That distinction matters: the word "the" appears constantly in English text, but it carries no semantic weight at all.
Extract keywords from competitors' top-ranking pages, then compare them to your own content. Missing terms are content gaps — opportunities to add depth that improves both relevance and rankings.
Paste a lecture transcript or textbook chapter. The extracted keywords become your study vocabulary — the terms most worth understanding deeply before an exam.
For content managers handling large volumes of articles, keyword extraction automates the tagging process. Extract keywords, use them as tags, and your content becomes searchable without manual categorisation.
Before committing to reading a full academic paper, extract keywords to verify it covers the concepts you need. Faster than reading abstracts, and more reliable for identifying conceptual coverage.
The extracted keywords often map directly to the most effective terms for page titles and meta descriptions — the vocabulary your audience actually uses when searching for the topic.
For a broader view of the text's content, pair this with the Text Summarizer — keywords tell you the vocabulary, the summary tells you the argument.
Between 8 and 15 keywords and key phrases, depending on the length and complexity of the text. Shorter texts return fewer; longer, topic-dense texts return more.
Both. Single important terms and meaningful multi-word phrases (like "machine learning" or "supply chain disruption") are included. Phrase extraction is often more useful than single-word extraction for research and SEO purposes.
It's useful for understanding what terms are prominent in a piece of content — helpful for on-page SEO analysis and content gap identification. For keyword research (search volume, competition data), you'll want a dedicated SEO tool like Ahrefs or Semrush.
Yes. Extracting keywords from a lecture transcript or textbook chapter is an effective study technique — the keywords become the anchors for a concept map or flashcard set.
Informational text works best — articles, reports, essays, transcripts. Fiction and poetry produce less meaningful keyword sets because the important "terms" are themes, not specific vocabulary.