Authors : Tanushree Bharti; Satyam Kumar Sinha; Harshit Singhal; Rohit Saini; Dipesh Parihar

Volume/Issue : Volume 9 - 2024, Issue 8 - August

Google Scholar : https://tinyurl.com/mry49tjb

Scribd : https://tinyurl.com/yz6yrwz5

DOI : https://doi.org/10.38124/ijisrt/IJISRT24AUG334

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : In the field of natural language processing, the task of writing long concepts into short expressions has attracted attention due to its ability to simplify the processing and understanding of information. While traditional transcription techniques are effective to some extent, they often fail to capture the essence and nuances of the original texts. This article explores a new approach to collecting abstract data using artificial neural networks (GANs), a class of deep learning models known for their ability to create patterns of real information. We describe the fundamentals of text collection through a comprehensive review of existing literature and methods and highlight the complexity of GAN-based text. Our goal is to transform complex text into context and meaning by combining the power of GANs with natural language understanding. We detail the design and training of an adaptive GAN model for the text recognition task. We also conduct various experiments and evaluations using established metrics such as ROUGE and BLEU scores to evaluate the effectiveness and efficiency of our approach. The results show that GANs can be used to improve the quality and consistency of generated content, data storage, data analysis paper, etc. It shows its promise in paving the way for advanced applications in fields. Through this research, we aim to contribute to the continued evolution of writing technology, providing insights and innovations that support the field to a new level of well-done.

Keywords : Generative Adversarial Networks (GANs), Natural Language Processing (NLP Text Generation, Machine Learning.

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