Text Summarizer

Text summarizer

Text summarization turns a long article, report, or set of notes into a shorter version that preserves its main points. This tool uses extractive summarization: it selects complete sentences from your text instead of inventing or rewriting content.

How the summarizer works

The browser analyzes each sentence using three signals:

1. Keyword frequency - repeated non-stop words indicate central topics.
2. Sentence position - opening sentences often introduce the subject, while closing sentences may contain conclusions.
3. Useful length - medium-length sentences usually contain more context than fragments or overly long sentences.

Selected sentences are returned in their original order. This keeps the result readable while making the selection process easy to inspect.

Adjust summary length

Use the summary-length slider to retain 10%–70% of the source sentences:

Setting	Best for
10%–25%	Quick previews and headlines
30%–50%	Articles, reports, and study notes
55%–70%	Detailed review with less compression

The tool keeps at least one sentence. The result also shows original sentence count, summary sentence count, reduction percentage, and top keywords.

Extractive vs. generative summaries

Approach	Strength	Limitation
Extractive	Quotes source text, fast, transparent, browser-only	May need editing for smooth transitions
Generative	Can rewrite and combine ideas	May add unsupported details or change meaning

Extractive output is useful when traceability matters. Compare it with the original before using it for legal, medical, financial, or other high-stakes work.

Common uses

• Preview a long article before reading it in full.
• Reduce meeting notes to key statements.
• Create a first-pass study guide.
• Identify repeated topics in a report.
• Prepare a concise brief while keeping source wording.

Browser-only processing

Text stays in the current browser tab. No server request or AI service is required, so the tool is suitable for quick drafts and private notes. Browser-only processing does not replace your organization’s privacy or retention policies.

Example workflow

1. Paste text containing complete sentences.
2. Choose a compression level.
3. Review the selected sentences and keywords.
4. Compare the summary with the source before sharing it.

Code examples

The same basic extractive pattern can be implemented in common languages:

JavaScript

const sentences = text.match(/[^.!?]+(?:[.!?]+|$)/g) ?? [];
const summary = sentences.slice(0, Math.max(1, Math.round(sentences.length * 0.3)));

Python

import re

sentences = [s.strip() for s in re.findall(r"[^.!?]+(?:[.!?]+|$)", text) if s.strip()]
summary = sentences[:max(1, round(len(sentences) * 0.3))]

These snippets demonstrate sentence selection only. A production summarizer should also normalize words, remove stop words, score relevance, and preserve source order.