Jul 2026· Artificial Intelligence and Applications· 0 citations· 40 references
TL;DR
The proposed framework using Bidirectional Long Short-Term Memory with a hypergraph and a dominating set mechanism proves to be an efficient approach to automatic summarization and has the potential to be applied in journalism, healthcare, legal analysis, and digital content management.
Abstract
Automatic text summarization condenses voluminous text while preserving crucial information. Despite significant advancements in this field, challenges persist in accurately identifying key content, maintaining contextual coherence, and ensuring computational efficiency. The existing approaches struggle to effectively capture global relationships between sentences and minimize redundancy. In this study, we propose an extractive text summarization framework using Bidirectional Long Short-Term Memory with a hypergraph and a dominating set mechanism. A neural encoding module derives context-aware sentence embeddings, and a hypergraph-based representation models higher-order relational dependencies among sentences. A dominating set-based selection strategy is then applied to identify the most informative sentences for summary generation. The proposed model is evaluated using the CNN/Daily Mail dataset and Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics. Empirical evaluation attains ROUGE-1, ROUGE-2, and ROUGE-L scores of 0.4221, 0.3563, and 0.3901, respectively, and demonstrates the proposed model’s capability to effectively capture key content and generate coherent and informative summaries. The proposed framework proves to be an efficient approach to automatic summarization and has the potential to be applied in journalism, healthcare, legal analysis, and digital content management.
Received: 12 February 2026 | Revised: 8 June 2026 | Accepted: 23 June 2026
Conflicts of Interest
The authors declare that they have no conflicts of interest to this work.
Data Availability Statement
The data that support the findings of this study are openly available in Kaggle at https://www.kaggle.com/datasets/gowrishankarp/newspaper-text-summarization-cnn-dailymail/data1.
Author Contribution Statement
Pradeepa Sampath: Conceptualization, Methodology, Software, Data curation, Writing – original draft, Writing – review & editing, Project administration. Shrijaa Venkatasubramanian Subashini: Conceptualization, Methodology, Software, Data curation, Writing – original draft, Writing – review & editing. Vimal Shanmuganathan: Validation, Formal analysis, Investigation, Resources, Visualization, Supervision. Seifedine Kadry: Validation, Formal analysis, Investigation, Resources, Visualization, Supervision.
AI-based text summarization plays a critical role in managing the rapidly increasing volume of textual information by enabling efficient information retrieval and concise knowledge representation. Conventional summarization approaches often rely on heuristic rules and surface-level features, limiting their ability to capture contextual relevance and semantic coherence in long documents. To overcome these limitations, this study develops a deep learning–based extractive text summarization framework using a hybrid Bidirectional Encoder Representations from Transformers (BERT) combined with Bidirectional Long Short-Term Memory (BiLSTM) architecture. The model leverages BERT’s bidirectional transformer encoder to generate rich contextual sentence embeddings and employs a BiLSTM network to model inter-sentence dependencies and document-level structure, enabling effective identification of summary-worthy sentences while preserving original semantic content. The framework is evaluated using the BBC News Summary dataset, which contains professionally written news articles and corresponding human reference summaries. Performance is assessed through sentence-level classification metrics and ROUGE-based evaluation. Experimental results demonstrate strong effectiveness, achieving an overall accuracy of 98.5%, with balanced precision (98.7%), recall (98.2%), and F1-score (98.45%), indicating reliable sentence importance classification. In addition, the model attains competitive ROUGE ((Recall-Oriented Understudy for Gisting Evaluation) scores (ROUGE-1 = 0.56, ROUGE-2 = 0.33, and ROUGE-L = 0.52), confirming substantial overlap with human summaries. These results highlight the robustness, generalization capability, and practical applicability of the suggested BERT–BiLSTM structure for automatic text summarization and knowledge representation.
Dr. RakeshKumar ER, S. Ramachandran, Ayushi Khandal et al.· 2026 7th International Confe...· 0 citations
Abstractive text summarization is a critical yet challenging task in natural language processing, requiring models to generate concise, coherent, and factually accurate summaries. Existing transformer-based architectures, such as BART and T5, often underperform in scenarios where language semantics evolve over time or when lexical diversity is limited. These limitations result in summaries that lack contextual relevance and semantic generalization, especially in dynamic domains such as news reporting. In this work, we present a lightweight hybrid abstractive summarization model that enhances temporal awareness and linguistic flexibility. The model integrates synonym-based augmentation and time-sensitive embeddings into a base transformer encoder-decoder architecture, enabling the generation of temporally aligned and semantically enriched summaries. To further improve reliability, we introduce a factual alignment evaluation module that assesses entity-level consistency using named entity recognition. We conduct extensive experiments on two benchmark datasets, XSum and CNN/Daily Mail, and evaluate the model using a wide range of metrics, including ROUGE, BLEU, METEOR, BERTScore, and factual consistency checks. The ablation study confirms the individual contributions of temporal modeling and synonym enrichment, with the combined model achieving up to 8.3% ROUGE-1 and 7.9% METEOR improvements over the baseline. The model demonstrates practical applicability for summarization systems requiring both temporal relevance and semantic robustness, while maintaining CPU efficiency and interpretability. Implications for downstream tasks like news trend analysis are also explored.
K. Abdalgader, Falah Y. H. Ahmed, Blen Wassie et al.· Discover Artificial Intellig...· 0 citations
This article investigates extractive and abstractive text summarization for the Azerbaijani language, a low-resource and underrepresented language in natural language processing. While the underlying modeling approaches are well established, their application to Azerbaijani summarization remains largely unexplored due to the scarcity of large-scale datasets and prior empirical studies. To address this gap, we conduct a systematic evaluation of both extractive methods based on sentence ranking and an abstractive approach using a fine-tuned mT5-base model. Our experiments are carried out on a large-scale dataset comprising over 115,000 Azerbaijani news articles paired with human-written summaries. The models are evaluated using standard automatic metrics, including Recall-Oriented Understudy for Gisting Evaluation (ROUGE), Bilingual Evaluation Understudy (BLEU), and Metric for Evaluation of Translation with Explicit ORdering (METEOR), yielding strong results that highlight the benefits of task specific fine-tuning for abstractive summarization, while also demonstrating the competitiveness of extractive baselines. In addition, we analyze the impact of long input sequences and discuss architectural and dataset-related limitations affecting performance. Overall, this study provides a comprehensive empirical baseline for Azerbaijani text summarization and serves as a reference point for future research in low-resource summarization and related Azerbaijani Natural Language Processing (NLP) applications.
Mir Amir Pashayev, S. Rustamov· PeerJ Computer Science· 0 citations
The rapid growth of academic literature has intensified the need for effective automatic text summarization techniques capable of producing concise and informative representations of scientific documents. While extractive methods are widely used, they are limited in their ability to generate coherent and semantically rich summaries. Recent advances in Transformer-based architectures have enabled significant progress in abstractive summarization; however, their effectiveness on domain-specific datasets, such as scientific articles, remains an open challenge. In this study, we investigate the performance of three pre-trained Transformer-based models—T5, BART, and GPT-2—on the task of abstractive summarization using the CL-SciSumm 2019 dataset. A total of 19 experimental configurations are conducted to analyze the impact of generation parameters, including beam size, length penalties, and n-gram constraints, on summarization quality. The models are evaluated using ROUGE metrics, with a focus on ROUGE-2.To complement content-based evaluation, this work incorporates linguistic acceptability assessment using the Corpus of Linguistic Acceptability (CoLA), a benchmark dataset for evaluating grammatical correctness. The results show that BART achieves the best performance with an ROUGE-2 F1-score of 0.40664, while T5 demonstrates superior grammatical acceptability, achieving 93.36%, but BART achieves a very near performance to T5. Ultimately, these findings demonstrate the potential of pre-trained neural networks, particularly the BART architecture, to drive the future of complex, generative NLP applications, transforming how academic research is processed and understood.
Emad Nabil· Islamic University Journal o...· 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.
The proposed MLOA-MA-ASeqNet architecture, a Multi-scale Attention and Adaptive Sequence-to-Sequence Network whose hierarchical encoder operates simultaneously at word, phrase and sentence level granularity, achieves the highest average score across fluency, adequacy, coherence and readability.
V. M, Kunal Chakma, Anupam Jamatia et al.· ACM Transactions on Asian an...· 0 citations