Jul 2026· International Scientific Journal of Engineering and Management· Vol 05, pp. 1-7· 0 citations
TL;DR
This thesis begins by establishing the background and motivation for the study, focusing on the growing importance of automatic text summarization and the challenges associated with factual inconsistencies in generated summaries, and critically reviews prior research in the field of text summarization.
Abstract
This thesis presents a detailed introduction to the research work. It begins by establishing the background and motivation for the study, focusing on the growing importance of automatic text summarization and the challenges associated with factual inconsistencies in generated summaries. The chapter critically reviews prior research in the field of text summarization, highlighting the limitations of traditional Automatic Text Summarization (ATS) systems, particularly in ensuring factual correctness. Furthermore, the chapter introduces Large Language Models (LLMs) and prompt engineering as emerging solutions capable of addressing these limitations. The objectives of the research, along with the key research questions, are clearly articulated to define the scope and direction of the study. The chapter also delineates the boundaries of the research by specifying the scope and assumptions considered. Finally, the significance of the study is discussed, emphasizing its contribution to improving evaluation methodologies for text summarization systems.
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Key Words: Natural Language Processing (NLP), Large Language Models (LLM), Chain of Thought (COT), Generative Pre-trained Transformers (GPT), Recall-Oriented Understudy
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.
Ahmed J. Jabur, Asmaa Abdul Azeez Dakhil, Israa Saad Mohammed et al.· Iraqi Journal for Computers...· 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.
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.
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
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 study resulted in the development of a novel six-component framework comprising Input Processing, LLM Core, Knowledge Enhancement, Context Management, Response Generation, Response Generation, and Human Feedback that successfully addressed resource scarcity through language detection and cross-lingual query understanding.
