{
  "@context": "https://schema.org",
  "@type": "article",
  "@id": "https://anchorfact.org/kb/text-summarization",
  "headline": "Text Summarization: From Extractive Methods to Abstractive LLM-Based Summarization",
  "description": "Text summarization condenses documents into concise summaries while preserving key information. The field has evolved from simple sentence extraction to LLM-powered abstractive generation that rewrites content in its own words. The hard problems remain: summarizing book-length documents, ensuring factual accuracy, and adapting summaries to user needs.",
  "dateCreated": "2026-05-24T02:49:13.666Z",
  "dateModified": "2026-05-24",
  "author": {
    "@type": "Organization",
    "name": "AnchorFact"
  },
  "publisher": {
    "@type": "Organization",
    "name": "AnchorFact",
    "url": "https://anchorfact.org"
  },
  "license": "https://creativecommons.org/licenses/by/4.0/",
  "anchorfact:confidence": "high",
  "anchorfact:generationMethod": "ai_assisted",
  "citation": [
    {
      "@type": "CreativeWork",
      "name": "A Systematic Survey of Text Summarization: From Statistical Methods to Large Language Models",
      "sameAs": "https://dl.acm.org/doi/abs/10.1145/3731445"
    },
    {
      "@type": "CreativeWork",
      "name": "A Systematic Survey of Text Summarization: From Statistical Methods to LLMs",
      "sameAs": "https://arxiv.org/abs/2406.11289"
    }
  ]
}