Text Summarizer
Summarize any text using the TextRank extractive algorithm. Selects the most important sentences using graph-based ranking — fully client-side with zero API calls.
How to Use
Summarize text in three steps:
- Paste your text — Enter an article, report, essay, or any document with at least 3-4 sentences. Click a sample button to try pre-loaded examples in technology, climate, and history.
- Adjust length — Use the sentence slider to choose how many sentences the summary should contain (1-10). Fewer sentences give a tighter summary; more sentences preserve more detail.
- Review results — See the extracted summary, compression statistics, and a full sentence ranking showing why each sentence was selected or skipped. Copy the summary with the clipboard button.
About This Tool
TextRank Algorithm
TextRank was introduced by Rada Mihalcea and Paul Tarau in 2004 as an unsupervised graph-based ranking algorithm for text processing. It builds a similarity graph where sentences are nodes and edges represent vocabulary overlap. The algorithm then applies an iterative ranking process inspired by Google's PageRank: sentences that are similar to many other important sentences receive higher scores. After convergence (typically 20-50 iterations), the top-ranked sentences form the summary.
The similarity between two sentences is computed as the count of shared content words divided by the logarithm of each sentence's length. This normalization prevents long sentences from dominating simply because they contain more words. A damping factor of 0.85 (same as PageRank's original value) ensures that some probability mass is distributed uniformly to all sentences, preventing dead ends in the graph.
Extractive vs Abstractive Summarization
This tool performs extractive summarization — it selects the most important sentences verbatim from the original text. The summary contains no generated or paraphrased text, which guarantees factual accuracy (no hallucination). The trade-off is that extractive summaries can feel choppy or miss nuances that require rephrasing. Abstractive summarization (used by GPT, Claude, and other LLMs) generates new text that paraphrases the source, producing more fluent summaries but requiring large neural models that are too heavy for client-side execution.
Sentence Scoring
The sentence ranking view shows each sentence's TextRank score on a 0-100 scale. Higher scores indicate sentences that are central to the text's topic network — they share vocabulary with many other important sentences. Selected sentences are highlighted in green. Sentences at the beginning and end of a text often score higher because they tend to introduce and conclude key themes. For keyword analysis of your text, see Keyword Extractor.
Why Use This Tool
Instant Client-Side Summarization
TextRank's graph construction and iteration run in under 100 milliseconds for typical documents (up to several thousand words). No model downloads, no API calls, no rate limits. The algorithm's computational complexity is O(n² × k) where n is the sentence count and k is the average sentence length — efficient enough for real-time interaction.
Common Use Cases
- Research triage: Quickly extract key findings from academic papers, reports, and white papers without reading the entire document.
- Meeting notes: Condense lengthy meeting transcripts into the most important discussion points and decisions.
- News digests: Summarize long-form articles into concise bullet-point summaries for rapid consumption.
- Study aids: Extract core concepts from textbook chapters or lecture notes for exam preparation.
- Content creation: Generate article abstracts, executive summaries, or social media excerpts from longer content.
Privacy
100% client-side processing. Your text never leaves your browser. Related tools: Readability Analyzer, Keyword Extractor, Sentiment Analyzer, and Word Counter.