Text Summarizer

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Condense long articles, essays, and documents into their most important sentences using extractive summarization — a purely algorithmic approach that requires no AI API or server connection. The algorithm is based on TF-IDF (Term Frequency–Inverse Document Frequency) sentence scoring: it first tokenizes the text, removes stop words (common words like 'the', 'and', 'is' that carry little meaning), and applies basic suffix stemming to group word variants together. Each unique term receives a TF-IDF score reflecting how characteristic it is of this specific document. Each sentence is then scored as the sum of the TF-IDF scores of its constituent words, normalized by sentence length to avoid bias toward longer sentences. The top N sentences — controlled by an output slider (1–10 sentences) — are returned as the summary in their original document order so the result reads naturally. In the source text view, summary sentences are highlighted in amber so you can see exactly which parts of the original were selected. The Key Phrases panel extracts the top 10 highest-scoring multi-word phrases (bigrams and trigrams) from the TF-IDF rankings, giving a quick overview of the document's main topics. Statistics show word count, sentence count, reading time for both the original and the summary, and the compression ratio.

Cómo usar

1. 1 Paste your article, essay, or document text into the input area.
2. 2 Use the 'Summary length' slider to choose how many sentences (1–10) you want in the summary.
3. 3 Click 'Summarize' to run the TF-IDF analysis.
4. 4 Read the extracted summary in the Summary tab.
5. 5 Switch to the Highlighted Text tab to see which sentences were selected within the original document.
6. 6 Check the Key Phrases panel for the top 10 significant terms and phrases.

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