1 Introduction
1.1 What is Natural Language Generation?
1.2 Why a Survey on Natural Language Generation?
1.3 Goals of this Survey
2 NLG Tasks
2.1 Content Determination
2.2 Text Structuring
2.3 Sentence Aggregation
2.4 Lexicalisation
2.5 Referring Expression Generation
2.6 Linguistic Realisation
2.7 Discussion
3 NLG Architectures and Approaches
3.1 Modular Approaches
3.2 Planning-Based Approaches
3.3 Other Stochastic Approaches to NLG
3.4 Discussion
4 The Vision-Language Interface: Image Captioning and Beyond
4.1 Data
4.2 The Core Tasks
4.3 How is Language Grounded in Visual Data?
4.4 Vision and Language: Current and Future Directions for NLG
5 Variation: Generating Text with Style, Personality and Affect
5.1 Generating with Style: Textual Variation and Personality
5.2 Generating with Feeling: Affect and Politeness
5.3 Stylistic Control as a Challenge for Neural NLG
5.4 Style and Affect: Concluding Remarks
6 Generating Creative and Entertaining Text
6.1 Generating Puns and Jokes
6.2 Generating Metaphors and Similes
6.3 Generating Narratives
6.4 Generating Creative Language: Concluding Remarks
7 Evaluation
7.1 Intrinsic Methods
7.2 Extrinsic Evaluation Methods
7.3 Black Box Versus Glass Box Evaluation
7.4 On the Relationship Between Evaluation Methods
7.5 Evaluation: Concluding Remarks
8 Discussion and Future Directions
8.1 Why (and How) Should NLG be Used?
8.2 Does NLG Include Text-to-Text?
8.3 Theories and Models in Search of Applications?
8.4 Where do We Go from Here?
9 Conclusion