Summarization

Summarization is the process of producing a concise representation of a source text that preserves its essential information and meaning. It aims to convey the main ideas and relevant details while reducing length. It is used across domains to facilitate quick comprehension and to support information retrieval, decision making, and content management.

There are two primary kinds: extractive summarization, which selects a subset of sentences from the original document to form the summary, and abstractive summarization, which generates new sentences that may omit or rephrase content. Hybrid approaches combine elements of both.

Methods range from traditional linguistic and statistical techniques—sentence scoring based on features such as position, term frequency, and cue words; graph-based ranking like TextRank—to modern neural models, including sequence-to-sequence architectures and large transformers trained to summarize. Pretrained models such as BART, T5, and Pegasus are commonly used, often fine-tuned on summarization datasets.

The typical pipeline includes preprocessing, content selection or generation, and post-processing. Extractive systems assemble selected sentences; abstractive systems produce fluent text with paraphrasing, sometimes at the cost of factual accuracy.

Evaluation can be intrinsic or extrinsic. Automatic metrics such as ROUGE measure overlap with reference summaries, while human evaluation assesses readability, coherence, and informativeness. Datasets for benchmarking include CNN/Daily Mail, DUC/TAC, XSum, and other domain-specific corpora.

Applications span news aggregation, legal and medical document summarization, academic literature reviews, and customer service chat summaries. Challenges include maintaining factual consistency, preserving core meaning, ensuring coherence, and handling long documents or multi-document summarization.