Aug 2026· Iraqi Journal for Computers and Informatics· 0 citations· 122 references
TL;DR
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.
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."
The design realization and evaluation of an Automated Summarization Tool (AST) is presented which is a document intelligence platform based on google gemini 2.5 flash that outperforms the strongest fine-tuned transformer baselines (PEGASUS, BART) by ~14 points and is clearly ahead of BERTSUM-ext (a strong transformer baseline), Pointer-Generator Network, TextRank.
K. Kumar, A. Amandeep, Dharmender Kumar et al.· International Journal of Inn...· 0 citations
The exponential growth of scientific literature has intensified the demand for automated summarization systems capable of producing abstracts that are both linguistically fluent and factually reliable. Existing approaches face a fundamental trade-off: encoder-decoder models such as BART and T5 maintain strong factual grounding but produce rigid, extractive outputs, while decoder-only large language models (LLMs) such as Llama and Gemma generate highly fluent text yet remain susceptible to hallucination. This paper proposes a two-stage Synergistic Hybrid Ensemble framework designed to resolve this dichotomy. In Stage 1, a fine-tuned BART-Large model generates a factually grounded scaffold draft from a structured input representation comprising the document title, key sentences, method highlights, and results summary. In Stage 2, a QLoRA-adapted Llama-3.2-1B model performs coherent rewriting and stylistic polishing by conditioning on both the scaffold draft and the original source document. Experiments conducted on the arXiv Scientific Research Papers Dataset using BERTScore and entailment-based Factual Consistency metrics demonstrate that the proposed ensemble achieves a Factual Consistency metrics demonstrate that the proposed ensemble achieves a Factual Consistency score of 0.9140, substantially outperforming BART-Large (0.2890) and Llama-3.2-1B (0.6630) individually. Although the ensemble incurs a marginal reduction in BERTScore (0.8980) relative to Llama-3.2-1B (0.9555), this trade-off is justified given the critical importance of factual reliability in high-stakes scientific discourse. These findings confirm that anchoring the generative capacity of decoder-only LLMs to verified factual scaffolds effectively mitigates hallucination risk, offering a scalable and reproducible solution for high-fidelity scientific abstract generation.
Geoffrey Antonio Arifin, Andrew Widyanata, Henry Lucky et al.· International Conference on...· 0 citations
Zero-shot summarization using Large Language Models (LLMs) has significantly advanced the abstractive summarization task by producing coherent and fluent summaries. However, underlying stochasticity of the large language models raises concerns about the stability and trustworthiness of the LLM-generated summaries. This issue has become increasingly important due to proliferation of LLM-generated summaries in educational settings, where students and researchers summarize complex academic materials in zero-shot manner. We propose a novel two-level diagnostic protocol for benchmarking LLM-summarizers based on the stability of the generated summaries. At the lower level, document-level stability analysis is performed over multiple LLM-summaries generated under controlled environment, and the stability coefficient is computed. Each generated summary is scored for semantic and factual alignment with the original document, enabling estimation of stability along more than one dimensions. At the next level, observations from a stratified sample of documents drawn from the corpus are consolidated to estimate the stability index of the LLM-summarizer, which is the proxy for its trustworthiness. Our empirical investigation of three LLM-summarizers across three genres of documents reveals statistically significant differences in the generation-level variability among LLMs across summary evaluation metrics. This study advances the LLM-summarization research by evidential recognition of the stability problem in LLM-summaries and motivates further research towards development of robust, reliable and trustworthy LLM-summarizers.
Vasudha Bhatnagar, Purnima Bindal, Vikas Kumar et al.· 0 citations
This work study large language model (LLM)-based simplification of scientific texts and presents a human-in-the-loop workflow that transforms expert summaries into more accessible versions for non-specialists.
This survey provides a comprehensive overview of recent advances in LLM-based evaluation, covering techniques, applications, and challenges across domains, with future directions emphasizing standardized protocols, uncertainty estimation, and human–AI collaboration.
M. Nadăş· Artificial Intelligence Revi...· 0 citations
The growing volume of scientific literature has driven the demand for
automated text summarization systems that are natural and factual. Extractive Text
Summarization methods are factually accurate in meaning; still, they can lead to a summary
that is not cohesive. On the other hand, abstractive summarization systems improve readability
but may introduce factual bias. The paper overcomes these shortcomings by creating a hybrid
text summarization system that combines extractive and abstractive methods to maximize both
quality and factual content.
The framework uses two unsupervised extractive models, HipoRank and PacSum, to
extract important sentences, which are then synthesized with the original input document's
introduction section and subjected to long-document transformer models, PEGASUSX and
LED, to generate abstract-style summaries.
Among the tested combinations, the HipoRank-LED configuration achieved the most balanced performance, with ROUGE-1: 0.440, ROUGE-2: 0.220, and ROUGE-L: 0.410 on the PubMed dataset. This combination occasionally produced summaries with greater abstractiveness than the human-written references.
Various experiments across the ScisummNet, ArXiv, and PubMed datasets show that
hybrid configurations are always better than extractive and abstractive ones. HipoRank-LED is
the most efficient model, with ROUGE-1 = 0.440, ROUGE-2 = 0.220, and ROUGE-L = 0.410
on PubMed.
Results indicate that combining extractive grounding with long-context
transformers improves informativeness and coherence and reduces hallucination errors. The
introduction-guided structured input also provides better global context for summarizing
complex scientific documents.
The findings indicate that the transformer-based abstraction, combined with an
extractive text summarization approach, can be a very useful, scalable, and domain-independent
model for approximating long scientific texts.