A Reassessment of TextRank: Graph-Based Extractive Summarization in the Era of Large Language Models- A Systematic Review

Abstract

Despite the emergence of Large Language Models (LLMs), graph-based extractive summarization and specifically the TextRank algorithm have retained architectural significance in high-stakes domains including legal, medical, scientific, and journalistic text processing. This survey presents a systematic review of 121 references spanning 2002 to 2026, tracing the evolution of TextRank-based approaches into hybrid LLM pipelines and advancing three qualified arguments.First, TextRank provides architectural advantages that persist even as LLM context windows grow: source-level provenance that grounded generation methods only partially replicate; approximately 75% token consumption reduction at any fixed per-token rate; and deterministic, auditable extraction consistent with emerging regulatory transparency requirements in legal and clinical domains.Second, LLMs exhibit hallucination rates of 15–30% on domain-specific text and produce no source audit trail; TextRank pre-filtering substantially mitigates both limitations, while grounded generation alternatives address them at the generation layer. Third, hybrid TextRank→LLM pipelines achieve ROUGE-1 = 0.41 on CNN/DailyMail and 0.54–0.76 on domain-specific benchmarks; cross-benchmark ROUGE comparisons are not valid across different benchmark types and should not be interpreted as a unified performance ranking. The performance comparisons are derived from the reviewed literature; the layered pipeline architecture, limitations taxonomy, and research agenda are original contributions of this survey."