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Introduction

Text summarization and question answering are crucial tasks in the field of natural language processing, particularly in the domain of technical support. In this thesis, we aim to explore and develop techniques for automatically summarizing text and answering questions related to technical support queries. The ability to efficiently summarize large amounts of text and provide accurate answers to user queries can greatly enhance the efficiency and effectiveness of technical support systems.

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 Text Summarization
2.2 Techniques for Text Summarization
2.3 Text Summarization in Technical Support
2.4 Overview of Question Answering
2.5 Techniques for Question Answering
2.6 Question Answering in Technical Support
2.7 Evaluation Metrics for Text Summarization
2.8 Evaluation Metrics for Question Answering
2.9 Challenges and Limitations in Text Summarization and Question Answering
2.10 Future Directions in Text Summarization and Question Answering

Chapter 3: Research Methodology

3.1 Data Collection
3.2 Preprocessing of Data
3.3 Text Summarization Model Development
3.4 Question Answering Model Development
3.5 Evaluation Methodology
3.6 Experimental Setup
3.7 Performance Metrics
3.8 Statistical Analysis

Chapter 4: Discussion of Findings

4.1 Results of Text Summarization Model
4.2 Results of Question Answering Model
4.3 Comparison of Different Models
4.4 Analysis of Performance Metrics
4.5 Discussion on Experimental Results
4.6 Implications of Findings
4.7 Limitations of the Study
4.8 Recommendations for Future Research

Chapter 5: Conclusion and Summary

5.1 Summary of Findings
5.2 Conclusion
5.3 Contributions of the Study
5.4 Practical Implications
5.5 Future Research Directions

Thesis Overview: Text Summarization and Question Answering for Technical Support

Text summarization and question answering are important tasks in the field of natural language processing, particularly in the domain of technical support. This thesis aims to explore and develop techniques for automatically summarizing text and answering questions related to technical support queries. The ability to efficiently summarize large amounts of text and provide accurate answers to user queries can greatly enhance the efficiency and effectiveness of technical support systems.

Chapter 1 provides an introduction to the study, including background information, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of terms. Chapter 2 presents a comprehensive review of the literature on text summarization and question answering, covering techniques, evaluation metrics, challenges, and future directions. Chapter 3 outlines the research methodology, including data collection, preprocessing, model development, evaluation methodology, experimental setup, performance metrics, and statistical analysis. Chapter 4 discusses the findings of the study, including the results of the text summarization and question answering models, comparison of different models, analysis of performance metrics, implications of the findings, limitations of the study, and recommendations for future research. Finally, Chapter 5 provides a conclusion and summary of the project, highlighting the key findings, contributions, practical implications, and future research directions.

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