A Survey on Deep Learning based Various Methods Analysis of Text Summarization

Rahul, *; Rauniyar, Shristi; Monika, .

doi:10.1109/icict48043.2020.9112474

Cited by 10 publications

(2 citation statements)

References 4 publications

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“…But the positive response output looks incredible. According to Shristi Rauniyar [3] in 2020. In this paper, they propose various techniques for text summarization, such as Fuzzy C-Means, Deep learning, Machine learning, Transformer, SEN analysis, word embedding, differential methods, graph-based methods, text participation, clustering, Cascade Forest, MOABC.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning based Text Abstraction

Chougule,

Dudhabale,

Havaldar

2023

IJRASET

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Text abstraction based on deep learning has proven to be a promising method for the task of extracting large amounts of text while preserving the most important information. This article provides an overview of text abstraction based on deep learning, highlighting various techniques and applications in this field. This article reviews the existing literature on text abstraction based on deep learning, focusing on various methods such as sentence compression, text summarization, and paraphrase, and compares their advantages and disadvantages. The article also describes various deep learning techniques used in the field, including neural networks, recurrent neural networks, and convolution of neural networks. In addition, this article presents studies on the effectiveness of deep learning-based text in a variety of applications, including journalism, finance, health, and education. The article discusses the challenges faced by the field, such as resolving ambiguity and ensuring consistency and readability in produced texts. Finally, this article discusses future directions and potential areas for further research in deep learning-based text abstraction. This questionnaire is useful for researchers and practitioners interested in text abstraction and applications based on deep learning. The article also explores the ethical implications of deep learning-based reading, particularly with regard to issues such as prejudice and privacy. The benefits of this technology must be weighed against the risks, and it is important to ensure that deep learning-based text is created and used responsibly

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Section: Related Workmentioning

confidence: 99%

Deep Learning based Text Abstraction

Chougule,

Dudhabale,

Havaldar

2023

IJRASET

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“…Alongside such rich sources of textual data, there are also essential textual data available in electronic books and novels, legal and biomedical documents, and scientific papers, amongst many others. In fact, and as an instance of the significant increase in today's internet data, 90% of the data on the internet has been created in the last couple of years [2]. Moreover, more than two billion websites are currently active and hosted somewhere on the internet.…”

Section: Introductionmentioning

confidence: 99%

Deep Transformer Language Models for Arabic Text Summarization: A Comparison Study

Chouikhi¹,

Alsuhaibani

2022

Applied Sciences

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Large text documents are sometimes challenging to understand and time-consuming to extract vital information from. These issues are addressed by automatic text summarizing techniques, which condense lengthy texts while preserving their key information. Thus, the development of automatic summarization systems capable of fulfilling the ever-increasing demands of textual data becomes of utmost importance. It is even more vital with complex natural languages. This study explores five State-Of-The-Art (SOTA) Arabic deep Transformer-based Language Models (TLMs) in the task of text summarization by adapting various text summarization datasets dedicated to Arabic. A comparison against deep learning and machine learning-based baseline models has also been conducted. Experimental results reveal the superiority of TLMs, specifically the PEAGASUS family, against the baseline approaches, with an average F1-score of 90% on several benchmark datasets.

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