I. INTRODUCTION
A. Contributions
II. METHODOLOGY
III. PROPOSED TAXONOMY
IV. NLG METHODS
A. NLG Input
B. Training of NLG Models
C. Training Datasets for NLG
V. EVALUATION OF NLG METHODS
A. Evaluators
B. Interactivity
C. Internality
D. Evaluation Measures
E. Evaluation Methods
F. Standards
VI. RELEVANT AI TECHNIQUES FOR NLG
A. Neural Networks (NN)
C. Combined AI Techniques
VII. NLG TASKS
A. NLG Tasks and Subtasks
VIII. STYLISED TEXT GENERATION
A. Creative Writing
B. Humour Generation
C. Rhetoric Generation
D. Generative Text Augmentation
IX. CONVERSATION
A. General Approaches
B. Task-Oriented Conversation Systems
C. Q&A Systems
X. REWRITING
A. Text Style Transfer
B. Summarisation
XI. TRANSLATION & INTERPRETATION
A. Language Translation
C. Speech Recognition
D. Explainability
E. Source Code Comment Generation
XII. DECEPTION & DETECTION OF GENERATED TEXT
A. Textual Deception
B. Detecting Deceptive Text
XIII. BIAS & OTHER CHALLENGES IN NEURAL NLG
A. Risks and Harms of Powerful PLMs
B. Bias in PLM-Based NLG Systems
C. Other Challenges in Neural NLG
XIV. CONCLUSION