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.