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Introduction:

Generative Adversarial Networks (GANs) have gained significant attention in recent years for their ability to generate realistic data samples, including images, audio, and text. In the context of text generation, GANs have been used to create human-like text with coherent structures and meaningful content. This thesis explores the application of GANs for text generation and aims to investigate their effectiveness in generating high-quality and diverse textual data.

Table of Contents:

Chapter 1: Introduction
1.1 Background of Generative Adversarial Networks
1.2 Objective of the Study
1.3 Limitation of the Study
1.4 Scope of the Study

Chapter 2: Literature Review
2.1 Overview of Text Generation Techniques
2.2 Introduction to Generative Adversarial Networks
2.3 Applications of GANs in Text Generation
2.4 Challenges and Limitations in Text Generation using GANs

Chapter 3: Research Methodology
3.1 Data Collection and Preprocessing
3.2 GAN Architecture Selection
3.3 Training Process and Hyperparameters
3.4 Evaluation Metrics for Text Generation

Chapter 4: Discussion of Findings
4.1 Performance Evaluation of GAN Models
4.2 Analysis of Generated Text Samples
4.3 Comparison with Existing Text Generation Techniques
4.4 Future Directions for Research in GAN-based Text Generation

Chapter 5: Conclusion and Summary
5.1 Summary of Key Findings
5.2 Contributions of the Study
5.3 Implications for Text Generation Research
5.4 Recommendations for Future Work

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