Deep Learning based Text Abstraction

Abstract

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