A Review of Text Summarization using Gated Neural Networks

Authors

  • Iqbal T Computer Science & IT, Central University of Jammu, Central University of Jammu, Jammu, India
  • Sambyal AS Computer Science & IT, Central University of Jammu, Central University of Jammu, Jammu, India
  • Devanand Computer Science & IT, Central University of Jammu, Central University of Jammu, Jammu, India

DOI:

https://doi.org/10.26438/ijcse/v6si3.5155

Keywords:

RNN, Sequence to Sequence, Vanishing gradient, LSTM, GRU, Deep Recurrent Generative Decoder, GANs

Abstract

There is an enormous amount of information available in the form of documents, articles, links, webpages, etc., which can't be read completely until effectively summarized. Different procedures are effectively used to separate the imperative information from data to produce summary. This paper gives a brief description of text summarization and deep learning approach called Recurrent Neural Networks (RNNs). Recent advances in Deep RNN methods like Sequence to Sequence, Generative Adversarial Networks, etc. show remarkable results for text summarization. Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks are discussed to overcome the problem of Vanishing or Exploding Gradient.

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Published

2025-11-13
CITATION
DOI: 10.26438/ijcse/v6si3.5155
Published: 2025-11-13

How to Cite

[1]
T. Iqbal, A. S. Sambyal, and Devanand, “A Review of Text Summarization using Gated Neural Networks”, Int. J. Comp. Sci. Eng., vol. 6, no. 3, pp. 51–55, Nov. 2025.

Issue

Vol. 6 No. 3 (2018): IJCSE Special Issue April Edition

Section

Review Article

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