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February 2, 2026International Journal of Asian Language Processing

Enhancing Text Summarization with Deep Learning: A Seq2Seq Model Approach Using Attention Mechanisms and Stacked-LSTM Networks

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Authors

SSSandip SarkarMRMousiki Singha Roy

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Overview

This research develops a Seq2Seq text summarization model to improve summary quality, suggesting practical enhancements.

Key Points

  • The aim is to improve text summarization through a deep learning Seq2Seq model, focusing on algorithm effectiveness and summary quality.
  • Developed a Seq2Seq model using deep learning techniques in Python.
  • Implemented attention mechanisms and stacked LSTM networks to collect contextual information.
  • Used iterative testing to optimize model design and hyperparameters.
  • Adopted beam search decoding for generating coherent summaries.
  • Evaluated model performance with BLEU score metrics.
  • Achieved summarization precision between 70-85%.
  • Demonstrated that attention methods significantly improved model performance.
  • Indicated that beam search decoding led to more coherent results compared to greedy approaches.

Cite This Study

Sarkar et al. (2026) studied this question.

synapsesocial.com/papers/6980fdc7c1c9540dea80f874https://doi.org/10.1142/s2717554526500062
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