Deep Learning for Automatic Text Summarization: Introductory part of Current Techniques

Deep Learning for Automatic Text Summarization: Introductory part of Current Techniques

1 Divya Amade and 2 Rajshri Lanjewar 3 Vivek Kumar Sinha

1 M. Tech Student, Dept. of CSE, Raipur Institute of Technology, Raipur, Chhattisgarh, India

2 Assistant Professor, Dept. of CSE, Raipur Institute of Technology, Raipur, Chhattisgarh, India

3 Head Of Dept. of CSE, Raipur Institute of Technology, Raipur, Chhattisgarh, India

Corresponding Author’s Email: 1 divya.amade@gmail.com, 2 rajeshri21@gmail.com, 3 sinha.vivekkumar7@gmail.com

Abstract:

A crucial job in natural language processing (NLP) is text summarization,s that aims to condense large volumes of text into shorter summaries while retaining the key information. Deep learning techniques have recently shown promising results in text summarization, outperforming traditional machine learning approaches. In this study, we give a thorough assessment of current studies on deep learning-based text summarization. We explore the most widely used deep learning architectures for text summarization, including transformer-based models, recurrent neural networks, and convolutional neural networks. We also review various attention mechanisms used to improve the quality of generated summaries. Additionally, we present an overview of recent datasets and evaluation metrics used to benchmark text summarization systems. We also discuss current challenges and future directions for research in text summarization using deep learning techniques, including the need for more diverse datasets and better evaluation metrics.

Keywords: Deep learning, LSTM, sequence-to-sequence models, RNN, GRU, reinforcement learning, transformers.