This work presents a lightweight hybrid abstractive summarization model that enhances temporal awareness and linguistic flexibility, and demonstrates practical applicability for summarization systems requiring both temporal relevance and semantic robustness, while maintaining CPU efficiency and interpretability.
Abstract
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.
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
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.
Emad Nabil· Islamic University Journal o...· 0 citations
Abstractive text summarization remains a fundamental challenge in Natural Language Processing (NLP), particularly for long documents that require models to preserve long-range dependencies and maintain semantic coherence. Although Transformer-based architectures have achieved strong summarization performance, their full self-attention mechanism scales quadratically with sequence length and often requires input truncation in long-context applications.
This study presents a Neural Turing Machine (NTM)-based framework for abstractive text summarization. The proposed architecture combines a two-layer Bidirectional Long Short-Term Memory (BiLSTM) controller with an addressable external memory bank. Differentiable read and write operations decouple contextual storage from recurrent computation, enabling the persistent retrieval of salient information across extended input sequences. Detailed preprocessing, implementation, training, decoding, and evaluation settings are provided to support reproducibility. The framework was evaluated on the CNN/Daily Mail benchmark and compared with LSTM, Transformer, and BART baselines, as well as published high-performing systems, including PEGASUS, SimCLS, and BRIO. Ablation studies, learning-rate sensitivity analysis, long-context evaluation, inference-time scaling, statistical testing, qualitative error analysis, and memory-access visualization were also conducted.
The proposed NTM model achieved ROUGE-1, ROUGE-2, ROUGE-L, and BLEU scores of 47.8, 23.5, 44.6, and 20.1, respectively. Under the controlled experimental protocol, it outperformed the evaluated LSTM, Transformer, and BART baselines. Comparisons with published results indicate that the model is competitive with recent high-performing summarization systems. The additional analyses demonstrate that the external memory mechanism improves contextual retention and summarization stability, particularly for longer input sequences, while exhibiting favorable inference-time scaling behavior.
These findings demonstrate that integrating an addressable external memory with a BiLSTM controller offers an effective approach to abstractive summarization, particularly when processing long documents. The proposed framework provides competitive summarization performance while reducing dependence on computationally expensive full self-attention. The results highlight the potential of external-memory architectures as a scalable and stable alternative for long-context text summarization.
K. Katti, Kartikeya Reddy Katti, Amanul Islam· Frontiers in Artificial Inte...· 0 citations
Fine-tuning abstractive summarization models requires high-quality annotated data. However, obtaining such corpora is expensive and time-consuming, as it requires human annotators to read and comprehend long documents to create accurate summaries. Active learning mitigates this issue by selecting only the most informative instances for annotation, allowing models to achieve competitive results with significantly fewer labels. However, the application of active learning to summarization remains under-explored, and existing studies often suffer from instability and significant computational bottlenecks. To overcome these challenges, we propose LOBSTER (LOss-BaSed acTivE leaRning), a novel active learning framework designed specifically for abstractive summarization. LOBSTER improves performance by prioritizing unlabeled instances semantically similar to the model's current high-loss training examples, enabling the model to explicitly correct its specific weaknesses. Our empirical evaluation across three benchmark datasets and two summarization backbone models demonstrates that LOBSTER consistently matches or outperforms current state-of-the-art approaches while achieving a query selection speedup of up to 665x.
M. Ioannou, Tatiana Passali, George Michalopoulos et al.· 0 citations
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.
Pradeepa Sampath, S. Subashini, V. Shanmuganathan et al.· Artificial Intelligence and...· 0 citations
This approach combines language-adaptive mixture-of-experts embeddings with graph neural networks that model discourse structure, addressing linguistic challenges across typologically diverse low-resource languages.
Xuan-Hung Le, Thi Toan Do, Hoang-Quynh Le· Annual International ACM SIG...· 0 citations
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