NLP Text Summarisation — Automated Document Summary System

I will build an automated text summarisation pipeline for your documents — covering extractive summarisation (selecting the most important sentences) for factual accuracy requirements and abstractive summarisation (generating new concise text) for readability, with configurable summary length, section-aware summarisation for long structured documents, and batch processing for high-volume document sets. Summarisation approach selection depends on your fidelity requirements — abstractive summarisation produces more readable summaries but introduces paraphrase risk; extractive summarisation guarantees every sentence in the summary appeared verbatim in the source document; the right choice depends on whether your readers are working in regulated or legal contexts.

The pipeline covers your choice of extractive (TextRank or SumBasic) or abstractive (LLM or PEGASUS) approach, section-aware chunking for long documents, configurable summary length, batch processing for volume, quality validation against source coverage, and an API endpoint for on-demand summarisation.