What is extractive summarization?

Extractive summarization selects and returns actual sentences from the original document — it does not generate new text. This contrasts with abstractive summarization (used by AI language models) which rewrites the content in new words. Extractive methods are deterministic, fast, and require no external API.

TF-IDF stands for Term Frequency–Inverse Document Frequency. Term frequency (TF) measures how often a word appears in the text. Inverse document frequency (IDF) down-weights words that appear in almost every sentence (since they are less informative). Together, TF-IDF gives high scores to words that are frequent in the document but concentrated in specific sentences.

Stop words are common words (the, a, is, in, of, and…) that appear in virtually every document and carry little semantic meaning. Removing them before scoring prevents sentences from scoring highly simply because they contain many function words.

Why does the summary appear in original order?

The selected sentences are re-sorted into their original document order to preserve narrative flow and context. Reading the top-scored sentences in score order would often produce an incoherent result.

How are key phrases extracted?

After TF-IDF scoring, the tool evaluates all adjacent word pairs (bigrams) and triples (trigrams) that do not cross sentence boundaries and are not composed entirely of stop words. Phrases are scored as the sum of their constituent word scores and the top 10 are listed.

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Text Summarizer

Summarize long articles and documents using extractive TF-IDF sentence scoring — no AI API required. Highlights summary sentences and extracts key phrases.

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关于此工具

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.

使用方法

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

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