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Introduction

In recent years, text summarization has gained significant attention in the field of natural language processing (NLP) due to the increasing volume of text data available on the internet. Text summarization aims to generate a concise and coherent summary of a given text while preserving its key information. The Seq2Seq model, based on the encoder-decoder architecture, has proven to be effective in various NLP tasks, including text summarization. This project aims to build a Seq2Seq model for text summarization to generate informative and concise summaries of texts.

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 Seq2Seq model in NLP
2.3 Previous studies on text summarization using Seq2Seq model
2.4 Evaluation metrics for text summarization
2.5 Challenges in text summarization
2.6 Data preprocessing techniques for text summarization
2.7 Attention mechanisms in Seq2Seq model
2.8 Transfer learning for text summarization
2.9 Comparison of different approaches in text summarization
2.10 Future research directions in text summarization

Chapter 3: System Design and Methodology
3.1 Data collection and preprocessing
3.2 Building the Seq2Seq model architecture
3.3 Training the model
3.4 Hyperparameter tuning
3.5 Evaluation metrics
3.6 Fine-tuning the model
3.7 Implementing attention mechanisms
3.8 Handling out-of-vocabulary words

Chapter 4: System Implementation
4.1 Setting up the development environment
4.2 Data preparation for training and testing
4.3 Building the Seq2Seq model using a deep learning framework
4.4 Training the model on a large dataset
4.5 Fine-tuning the model on a specific domain
4.6 Testing the model on new data
4.7 Performance evaluation
4.8 Comparison with other text summarization approaches

Chapter 5: Conclusion and Summary
5.1 Summary of the project
5.2 Achievements and contributions
5.3 Limitations and future work
5.4 Conclusion

Thesis Overview: Building a Seq2Seq Model for Text Summarization

Text summarization is a challenging task in natural language processing that aims to generate a concise and coherent summary of a given text while preserving its key information. The Seq2Seq model, based on the encoder-decoder architecture, has shown promising results in text summarization tasks. This thesis focuses on building a Seq2Seq model for text summarization to generate informative and concise summaries of texts.

Chapter 1 provides an introduction to the thesis, including the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of terms. Chapter 2 presents a comprehensive literature review on text summarization, the Seq2Seq model, previous studies, evaluation metrics, challenges, data preprocessing techniques, attention mechanisms, transfer learning, and future research directions.

Chapter 3 outlines the system design and methodology, including data collection and preprocessing, building the Seq2Seq model architecture, training, hyperparameter tuning, evaluation metrics, fine-tuning, attention mechanisms, and handling out-of-vocabulary words. Chapter 4 covers the system implementation, such as setting up the development environment, data preparation, building the model using a deep learning framework, training on a large dataset, fine-tuning on a specific domain, testing, performance evaluation, and comparison with other approaches.

Chapter 5 concludes the thesis with a summary of the project, achievements, contributions, limitations, future work, and overall conclusion. This thesis aims to contribute to the advancement of text summarization techniques using the Seq2Seq model and to provide valuable insights for researchers and practitioners in the field of natural language processing.

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