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Introduction:

Text summarization and question answering are two important tasks in the field of natural language processing (NLP). With the exponential growth of textual data on the internet, there is a pressing need for automated systems that can summarize large amounts of text and answer questions based on that information.

Deep learning has emerged as a powerful tool for solving complex NLP tasks due to its ability to automatically learn representations of text data. In recent years, deep learning models have achieved state-of-the-art performance on various NLP tasks, including text summarization and question answering.

This thesis aims to investigate the application of deep learning techniques to text summarization and question answering tasks. Specifically, we will explore how neural networks can be trained to generate concise summaries of text documents and answer questions based on the content of those documents.

Chapter One: 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 Two: Literature Review
2.1 Overview of Text Summarization
2.2 Approaches to Text Summarization
2.3 Deep Learning for Text Summarization
2.4 Overview of Question Answering
2.5 Approaches to Question Answering
2.6 Deep Learning for Question Answering
2.7 Comparison of Text Summarization and Question Answering
2.8 Previous Studies on Text Summarization and Question Answering
2.9 Challenges and Limitations in Existing Approaches
2.10 Gaps in Literature

Chapter Three: Research Methodology
3.1 Data Collection
3.2 Data Preprocessing
3.3 Model Selection
3.4 Model Training
3.5 Evaluation Metrics
3.6 Experimental Setup
3.7 Hyperparameter Tuning
3.8 Performance Evaluation
3.9 Ethical Considerations

Chapter Four: Discussion of Findings
4.1 Analysis of Results
4.2 Comparison with Existing Methods
4.3 Interpretation of Performance Metrics
4.4 Impact of Data Preprocessing on Results
4.5 Limitations of the Proposed Approach
4.6 Future Directions for Research
4.7 Practical Implications
4.8 Contribution to the Field

Chapter Five: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions of the Study
5.3 Implications for Future Research
5.4 Recommendations for Practitioners
5.5 Conclusion

Thesis Overview on Text Summarization and Question Answering using Deep Learning:

The exponential growth of textual data on the internet has led to an increasing demand for automated systems that can summarize large amounts of text and answer questions based on that information. In recent years, deep learning has emerged as a powerful tool for solving complex natural language processing tasks, including text summarization and question answering. This thesis aims to investigate the application of deep learning techniques to text summarization and question answering tasks, with the goal of developing more accurate and efficient models for these tasks.

Chapter One provides an introduction to the research topic, outlining the background of the study, stating the problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of terms. Chapter Two presents a comprehensive literature review on text summarization and question answering, including an overview of existing approaches, deep learning techniques, comparisons, previous studies, challenges, and gaps in the literature.

Chapter Three details the research methodology, including data collection, preprocessing, model selection, training, evaluation metrics, experimental setup, hyperparameter tuning, performance evaluation, and ethical considerations. Chapter Four discusses the findings of the study, analyzing results, comparing with existing methods, interpreting performance metrics, addressing the impact of data preprocessing, highlighting limitations, suggesting future research directions, practical implications, and contributions to the field.

Chapter Five concludes the thesis with a summary of findings, contributions, implications for future research, recommendations for practitioners, and a final conclusion. The overall goal of this thesis is to advance the state-of-the-art in text summarization and question answering using deep learning techniques, ultimately contributing to the development of more efficient and accurate NLP systems.

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