AI Text Summarizer

The summarizer handles news articles, research papers, legal documents, meeting transcripts, blog posts, product reviews, and long emails. It works best with structured prose. Code, tables and binary files cannot be meaningfully summarized. For PDFs or web pages, copy-paste the text content first.

A good rule of thumb is 10–20% of the original length. A 1,000-word article becomes a 100–200-word summary. For executive summaries of long reports, aim for a single page regardless of the original length. For social sharing, aim for 2–3 sentences.

Extractive summarization picks the most important sentences directly from the source text. Abstractive summarization (what LLMs do) rephrases and condenses ideas in new language. Abstractive summaries are more natural but can occasionally introduce errors if the model misunderstands the source.

Yes, the tool handles documents up to the LLM context window limit (typically 100,000+ tokens). For very long documents like full books, summarize chapter by chapter and then summarize the summaries. This hierarchical approach produces better results than sending everything at once.