AutoSummarization

Autosummarization, also called automatic text summarization, is a subfield of natural language processing that aims to produce a shorter version of a text while preserving its main ideas. The goal is to reduce length while retaining essential information, structure, and tone. Summaries can be extractive, selecting existing sentences, or abstractive, generating new phrasing.

Extractive methods rely on ranking sentences by importance using features such as term frequency, sentence position, and cohesion cues, or on graph-based algorithms that propagate salience scores across sentences. Abstractive methods use neural networks to rewrite content, paraphrase, and combine information from multiple parts of the source.

Common approaches include graph-based systems such as TextRank, as well as supervised models using machine learning, and modern neural architectures based on sequence-to-sequence and transformer technologies. More recently, large language models can produce summaries via prompt-based generation.

Evaluation typically uses ROUGE scores that compare system output with reference summaries, along with human judgments of adequacy, coherence, and factual faithfulness. Datasets such as CNN/Daily Mail, XSum, and Newsroom are commonly used for benchmarking.

Applications span news aggregation, document summarization in legal and medical domains, academic abstracts, search engine results, and accessibility tools for readers with limited time or attention.

Limitations include potential factual inaccuracies or hallucinations in abstractive systems, bias, domain dependence, computational cost, and the need for high-quality training data. Ongoing work seeks better factual grounding and multilingual capabilities.

The field evolved from rule-based and statistical methods in the mid-20th century to neural and neural-symbolic approaches in the 2010s, and more recently to large language models that enable flexible, end-to-end summarization across domains.