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AI · NLP

AI Text Summarizer

Generic extractive summarizer that picks the most important sentences using a relevance measure and Latent Semantic Analysis.

The problem

Reading long articles and reports takes time. A good summary keeps the few sentences that carry the main ideas — without needing a human to pick them.

What I built

A generic, extractive text summarizer in Python that combines two classic techniques: a relevance measure that ranks sentences by how well they represent the whole document, and Latent Semantic Analysis (via singular value decomposition) that uncovers the underlying topics and selects the sentences that cover them best.

Outcome

Any text in, a short, faithful summary out — the same building blocks used in today’s document-processing and AI pipelines.

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