sumarizaci

Sumarizaci is a term used in Czech to denote the process of summarization, i.e., producing a concise and coherent version of a source text or dataset that preserves its essential information and meaning. It is applied in journalism, law, academia, and information technology. There are two principal approaches: extractive summarization, which selects existing sentences or passages; and abstractive summarization, which generates new sentences that paraphrase the content.

Techniques range from rule-based and statistical methods to modern neural models. Early systems used cue words, frequency analysis, and graph-based ranking (for example, TextRank, LexRank). Contemporary approaches rely on transformer-based architectures (BERT-like encoders, GPT, T5) fine-tuned for summarization. Abstractive systems aim for fluency and conciseness but must guard against factual inaccuracies, while extractive systems retain source wording and can be limited by the quality of the input.

Evaluation typically uses automatic metrics such as ROUGE, which compare overlaps with human reference summaries, supplemented by human judgments of readability and informativeness.

History: summarization concepts date to information retrieval research in the mid-20th century, with significant advances in the 1990s–2010s and rapid progress with deep learning in the last decade.

Applications include summarizing news articles, legal documents, scientific papers, customer reviews, and digital conversations. Challenges include maintaining factual fidelity, coherence, handling domain-specific language, and scalability to very long documents. Ethical considerations concern bias, misinformation, and copyright.

See also: extractive summarization, abstractive summarization, natural language processing, ROUGE.