Automatic Text Summarization Using Deep Learning     (compare the neural network model RNN, LSTM)

Divya Amade; Mahadev Bag; Rashmi Chandra

doi:10.59367/d7cvzw54

Authors

Divya Amade Department of Computer Science and Engineering, Raipur Institute of Technology
Mahadev Bag Department of Computer Science and Engineering, Raipur Institute of Technology
Rashmi Chandra Department of Computer Science and Engineering, Raipur Institute of Technology

DOI:

https://doi.org/10.59367/d7cvzw54

Keywords:

LSTM, RNN, ATS, abstractive, extractive

Abstract

Automatic text summarization using Deep learning is very essential way for summarization large content into summarize form. This paper presents a method of achieving text summaries accurately using deep learning methods, we propose a method of text summarization which focuses on the problem of identifying the most important portions of the text and producing coherent summaries with the help of deep learning. Deep learning techniques are proved to be effective in generating summaries form of volume text. The study explores both extractive and abstractive summarization methods using sequence to sequence model and LSTM. The research findings reveal the strengths and limitations of LSTM in text summarization and demonstrate its potential for facilitating efficient information extraction from textual data.

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