1. Introduction  
1.1. Overview of Deep Learning for Text Summarization  
1.2. Paper Structure  

2. Tasks in Summarization  
2.1. Summarization Method: Extractive vs Abstractive  
2.2. Source Document Quantity: Single-document vs Multi-document  
2.3. Source Document Length: Short vs Long  
2.4. Summary Length: Headline vs Short vs Long  
2.5. Language: Single-Language vs Multi-Language vs Cross-Lingual  
2.6. Domain: General vs Specific domain  
2.7. Level of Abstraction: Generic vs Query-focused  

3. Deep Learning Techniques  
3.1. Plain Neural Network  
3.2. Recurrent Neural Network  
3.3. Convolutional Neural Network  
3.4. Graph Neural Networks  
3.5. Transformer  
3.6. Reinforcement Learning  

4. Data and Experimental Performance Analysis  

5. Summary  
5.1. Challenge and Future  
5.2. Conclusion