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

Automated Summarization Tool

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.

Synergistic Summarization: An Ensemble of Fine-Tuned and Large-Scale Language Models for Factual and Coherent Scientific Abstracts

Reexamining zero-shot summarization: Empirical investigation of trustworthiness of LLM-summarizers

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.

A Human-in-the-Loop Corpus for LLM-Based Simplification of Scientific Summaries

Interdisciplinary research is accelerating, yet scientific papers remain difficult to understand outside their home fields. We study large language model (LLM)-based simplification of scientific texts and present a human-in-the-loop workflow that transforms expert summaries into more accessible versions for non-specialists. Using SciSummNet as the source corpus, we first generate baseline simplifications with GPT-4o-mini. In Phase 1, readers from STEM fields outside computer science identify difficult sentences and phrases and compare the original and GPT-simplified summaries in terms of comprehensibility, naturalness, and simplicity. In Phase 2, computer science experts use this feedback to create expert-edited reference simplifications. We release the resulting corpus together with human judgments and automatic evaluation results. The Phase 1 judgments show a clear preference for the GPT-generated summaries in terms of comprehensibility and simplicity, while qualitative analysis of the Phase 2 edits highlights the importance of preserving domain-specific terminology and the strength of scientific claims. The resulting resource supports the training and benchmarking of simplification systems for cross-disciplinary scientific communication.

Large language models as judges: recent advances in LLM-based evaluation, critique, preference modeling, and feedback for text and code

Large Language Models (LLMs) are increasingly used as judges to evaluate, rank, and critique AI-generated text and code. This survey provides a comprehensive overview of recent advances (2020–early 2026) in LLM-based evaluation, covering techniques, applications, and challenges across domains. We make three main contributions: (1) a unified taxonomy of LLM judging tasks spanning text (summarization, dialogue, factuality, safety) and code (correctness checking, code review, security analysis); (2) a systematic review of prompting strategies (zero/few-shot, rubric-based, pairwise comparison, chain-of-thought) and advanced pipelines (ensemble judges, multi-agent debate, tool-augmented verification); and (3) an analysis of LLM judge quality, documenting systematic biases (length, position, self-preference) and their mitigations. We review practical applications including benchmark evaluation (MT-Bench, Chatbot Arena), data filtering, and reward modeling for RLHF/RLAIF. Key challenges discussed include calibration, fairness, reproducibility, and adversarial robustness. We conclude with future directions emphasizing standardized protocols, uncertainty estimation, and human–AI collaboration. LLM-based judging shows promise for scalable evaluation, but careful design and rigorous validation are essential to ensure these AI judges meet human standards of accuracy and fairness.

Efficient Scientific Paper Summarization Using Unsupervised Extraction and Transformer-Based Abstraction

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.