Experiments on traditional Mongolian text summarization tasks demonstrate that the proposed Nucleus-to-Periphery Retrieval-Iterative Prompting method significantly out-performs multiple baseline models on automatic evaluation metrics including ROUGE-1, ROUGE-2, and ROUGE-L.
The exponential growth of scientific literature has intensified the demand for automated summarization systems capable of producing abstracts that are both linguistically fluent and factually reliable. Existing approaches face a fundamental trade-off: encoder-decoder models such as BART and T5 maintain strong factual grounding but produce rigid, extractive outputs, while decoder-only large language models (LLMs) such as Llama and Gemma generate highly fluent text yet remain susceptible to hallucination. This paper proposes a two-stage Synergistic Hybrid Ensemble framework designed to resolve this dichotomy. In Stage 1, a fine-tuned BART-Large model generates a factually grounded scaffold draft from a structured input representation comprising the document title, key sentences, method highlights, and results summary. In Stage 2, a QLoRA-adapted Llama-3.2-1B model performs coherent rewriting and stylistic polishing by conditioning on both the scaffold draft and the original source document. Experiments conducted on the arXiv Scientific Research Papers Dataset using BERTScore and entailment-based Factual Consistency metrics demonstrate that the proposed ensemble achieves a Factual Consistency metrics demonstrate that the proposed ensemble achieves a Factual Consistency score of 0.9140, substantially outperforming BART-Large (0.2890) and Llama-3.2-1B (0.6630) individually. Although the ensemble incurs a marginal reduction in BERTScore (0.8980) relative to Llama-3.2-1B (0.9555), this trade-off is justified given the critical importance of factual reliability in high-stakes scientific discourse. These findings confirm that anchoring the generative capacity of decoder-only LLMs to verified factual scaffolds effectively mitigates hallucination risk, offering a scalable and reproducible solution for high-fidelity scientific abstract generation.
Geoffrey Antonio Arifin, Andrew Widyanata, Henry Lucky et al.· International Conference on...· 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
It is found that cue-based prompting can influence multilingual sentence-level Easy-to-Read simplification, but its benefits are modest, metric-dependent, and language-dependent.
Mehrzad Tareh, Horacio Saggion, Stefan Bott· 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
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
Findings support the complete framework on the evaluated corpus, while the controlled comparisons indicate a modest complementary contribution from the BiLSTM and do not identify it as the sole source of the performance gains.
Shweta Bansal, S. Yogarayan, S. F. Abdul Razak· Information· 0 citations
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