Text Tools
Extractive Text Summarizer - TF-IDF Key Sentence Extractor
Automatically summarize long text by extracting the most important sentences using TF-IDF scoring. Adjust the number of sentences to keep and copy the summary.
How extractive summarization works
This tool uses extractive summarization: it ranks sentences in the original text by importance and selects the top N sentences to form the summary. Sentences appear in the summary exactly as in the original - no rewriting. This contrasts with abstractive summarization (used by AI language models), which generates new phrasing.
Sentence ranking methods
- TF-IDF (Term Frequency–Inverse Document Frequency): sentences containing words that appear frequently in this document but rarely in general text are considered more relevant.
- TextRank: a graph-based algorithm similar to Google’s PageRank. Sentences are nodes; edges represent shared vocabulary. Highly-connected sentences rank highest.
- Position bias: news articles and academic abstracts typically place the most important information in the first and last paragraphs.
When to use extractive summarization
- Quickly skimming long articles, legal documents, or research papers.
- Identifying key sentences before reading the full document.
- Creating bullet-point notes from dense prose.
Limitations of extractive summarization
Extractive methods work well on structured prose but have known weaknesses:
- Distributed information: arguments built up across multiple paragraphs may not be captured by any single sentence.
- Short texts: texts under 5 sentences have little to summarize; most sentences will be selected anyway.
- Non-prose content: lists, tables, source code, and highly structured documents are not well handled by sentence-ranking approaches.
Use cases with examples
| Content type | Expected result |
|---|---|
| Academic paper (5–20 pages) | Works well - abstract and conclusions are typically high-scoring sentences |
| Meeting transcript (Q&A format) | Moderate - key decisions may be captured, but dialogue is fragmented |
| Narrative fiction / story | Poor - important events are spread through description, not high-TF sentences |
AI summarization comparison
Large language models (GPT-4, Claude, Gemini) perform abstractive summarization: they paraphrase and synthesize information, generating new sentences not found in the source. This produces more coherent and readable summaries for complex content.
The trade-off is hallucination risk: LLMs can confidently include details that were not in the source. Extractive summarization, by contrast, only surfaces sentences that actually appear in the text - every sentence in the summary can be verified in the original document. For use cases where accuracy and verifiability matter more than fluency, extractive summarization remains the more trustworthy choice.