Summarizer

A summarizer is a person, a system, or a tool that produces a shortened version of content while preserving its essential information and meaning. Summarization can be applied to text documents, web pages, audio, images, or video transcripts. The goal is to convey the main ideas with reduced length, enabling faster reading or understanding.

There are two broad categories: extractive and abstractive summarization. Extractive approaches select a subset of existing sentences or phrases from the source, assembling a summary without generating new text. Abstractive approaches generate novel text that paraphrases the source while aiming to retain core meaning. Hybrid methods combine elements of both.

Techniques range from traditional statistics and graph-based methods, such as TF-IDF scoring and TextRank, to modern neural models based on sequence-to-sequence architectures and transformers. Evaluation commonly uses ROUGE scores comparing machine summaries to reference summaries, though human assessments remain important due to quality and factual accuracy.

Common applications include news briefings, document summarization in business or research, legal and medical document review, and accessibility tools. Limitations include potential loss of nuance, factual inaccuracies in abstractive systems, and biases in training data. Ethical and privacy considerations arise when summarizing sensitive material.