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**Introduction**

Automated text summarization and simplification are essential tasks in natural language processing (NLP) that can significantly improve the efficiency and accessibility of textual information. In recent years, there has been a growing interest in developing NLP techniques for automatically generating summaries and simplified versions of text to help users quickly grasp the main points of lengthy documents.

This thesis aims to investigate the use of NLP for automated text summarization and simplification, with the goal of developing effective and accurate techniques for these tasks. The study will explore various NLP approaches and algorithms that can be used to summarize and simplify text, as well as evaluate their performance and effectiveness. Additionally, the thesis will examine the challenges and limitations of current NLP systems for text summarization and simplification, and propose potential solutions to address these issues.

**Table of Contents**

*Chapter 1: Introduction*

1.1 Introduction
1.2 Background of study
1.3 Problem Statement
1.4 Objective of study
1.5 Limitation of study
1.6 Scope of study
1.7 Significance of study
1.8 Structure of the Thesis
1.9 Definition of terms

*Chapter 2: Literature Review*

2.1 Overview of Natural Language Processing
2.2 Text Summarization Techniques
2.3 Text Simplification Techniques
2.4 Evaluation Metrics for Summarization and Simplification
2.5 Applications of Automated Text Summarization and Simplification
2.6 Challenges in Text Summarization and Simplification
2.7 Previous Studies in NLP for Summarization and Simplification
2.8 State-of-the-Art NLP Models for Text Processing
2.9 Summary of Literature Review
2.10 Gaps in Existing Research

*Chapter 3: Research Methodology*

3.1 Data Collection and Preparation
3.2 Preprocessing and Feature Extraction
3.3 Algorithm Selection and Implementation
3.4 Evaluation Criteria
3.5 Experimental Setup
3.6 Performance Metrics
3.7 Statistical Analysis Methods
3.8 Ethical Considerations

*Chapter 4: Discussion of Findings*

4.1 Analysis of Experimental Results
4.2 Comparison of NLP Techniques
4.3 Interpretation of Performance Metrics
4.4 Discussion on Challenges and Limitations
4.5 Suggestions for Improving NLP Models
4.6 Implications for Future Research
4.7 Validation of Hypotheses
4.8 Contributions to the Field
4.9 Practical Applications of Research
4.10 Conclusion of Findings

*Chapter 5: Conclusion and Summary*

5.1 Summary of Research Questions
5.2 Summary of Findings
5.3 Contribution to Knowledge
5.4 Practical Implications
5.5 Limitations and Future Directions
5.6 Conclusion

**Thesis Overview**

Automated text summarization and simplification have become increasingly important in the field of natural language processing, as they can enhance the accessibility and usability of textual information. This thesis aims to investigate the use of NLP techniques for automating these tasks, with the goal of developing efficient and accurate methods for summarizing and simplifying text.

Chapter 1 provides an introduction to the research topic, including background information, the problem statement, objectives, limitations, scope, significance, and the structure of the thesis. Chapter 2 presents a comprehensive literature review on NLP, text summarization, text simplification, evaluation metrics, applications, challenges, previous studies, state-of-the-art models, and gaps in existing research.

Chapter 3 outlines the research methodology, including data collection, preprocessing, algorithm selection, evaluation criteria, experimental setup, performance metrics, statistical analysis, and ethical considerations. Chapter 4 discusses the findings of the research, analyzing experimental results, comparing NLP techniques, interpreting performance metrics, addressing challenges, proposing improvements, and suggesting future research directions.

In Chapter 5, the thesis concludes with a summary of research questions, findings, contributions to knowledge, practical implications, limitations, and future directions. The thesis aims to advance the field of NLP by exploring innovative approaches to automated text summarization and simplification, with the potential to impact various applications in information retrieval, text mining, and natural language understanding.

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