Jul 2026· 2026 7th International Conference on Smart Systems and Inventive Technology (ICSSIT)· pp. 1731-1737· 0 citations· 21 references
Abstract
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.
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
The rapid growth of digital textual content has intensified the need for automatic text summarization sys- tems that are both effective and reliable. While extractive summarization methods are interpretable and preserve factual content, they often suffer from redundancy and limited coherence. In contrast, abstractive approaches based on large pretrained transformer models improve fluency and readability but are prone to factual inconsistencies and hallucination. To address these limitations, this paper proposes a semantic- aware hybrid text summarization framework that integrates supervised extractive sentence scoring with constrained abstractive generation. The proposed approach employs an interpretable sentence importance model based on lexical, positional, and semantic features, learned using a Gradient Boosting Regressor. Semantic redundancy among candidate sentences is explicitly controlled using Word Mover’s Distance, enabling improved content diversity without increasing training complexity. The selected sentences are subsequently refined using a transformer-based abstractive model to enhance coherence and linguistic quality while preserving factual consistency. The framework is evaluated on a benchmark news summa- rization dataset using ROUGE-1, ROUGE-2, and ROUGE-L metrics. Experimental results demonstrate consistent improvements over a purely extractive baseline and competitive performance compared to repre- sentative extractive, abstractive, and hybrid approaches. Ablation studies further confirm the contribution of supervised sentence scoring, semantic redundancy control, and abstractive refinement to performance stability. Overall, the results indicate that combining interpretable sentence selection with semantic simi- larity modeling within a hybrid architecture provides a balanced and practical solution for automatic text summarization.
Khaoula Belila, Nedjoua Houda Kholladi, Mohammed Bedida et al.· Informatica· 0 citations
Introduction 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. Methods 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. Results 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. Discussion 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, K. Katti, Amanul Islam· Frontiers in Artificial Inte...· 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
Text-based visual question answering (TextVQA) requires reasoning over images containing rich textual content, often involving knowledge beyond what is directly observable. Existing methods fuse visual objects and OCR tokens but struggle when questions require external knowledge. Moreover, naively incorporating retrieved knowledge often introduces irrelevant or misleading information, which may hinder reasoning rather than support it. To address these challenges, we propose a TextVQA framework that integrates external prior knowledge to support multimodal reasoning. Given an image and question, a task-aware knowledge retrieval module selects relevant candidates, which are then filtered and verified by a knowledge verification module leveraging large language models. The verified knowledge and question are compressed into compact embeddings via a perceiver-based semantic resampler and jointly processed with visual and OCR features in a multimodal reasoning module. Experiments on the TextVQA and ST-VQA datasets demonstrate that our approach effectively leverages external knowledge to improve performance on knowledge-intensive questions.
This work proposes LOBSTER, a novel active learning framework designed specifically for abstractive summarization that 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.
M. Ioannou, Tatiana Passali, George Michalopoulos et al.· 0 citations
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