Web Summarisation

Web Summarisation employs Natural Language Processing (NLP) and transformer models (e.g., GPT-3) to computationally condense lengthy web content into a concise, context-preserving summary.

This technology is a critical AI application: it processes vast digital content—articles, reports, PDFs—to mitigate information overload. Systems utilize two primary methods: extractive summarization, which selects and stitches together the most informative original sentences; and abstractive summarization, which generates entirely new, human-like text to capture core meaning. Modern models, like those based on the BERT architecture, efficiently reduce documents by up to 80%, allowing professionals and researchers to quickly digest a 50,000-word paper into a few essential paragraphs.

https://en.wikipedia.org/wiki/Automatic_summarization