Deep learning approaches to text production List of Figures List of Tables Preface Introduction Pre Neural Approaches Deep Learning Frameworks Generating Better Text Building Better Input Representations Modelling Task Specific Communication Goals Data Sets and Challenges Conclusion Bibliography Authors Biographies. Text production has many applications. It is used for instance to generate dialogue turns from dialogue moves verbalise the content of knowledge bases or generate English sentences from rich linguistic representations such as dependency trees or abstract meaning representations. Text production is also at work in text to text transformations such as sentence compression sentence fusion paraphrasing sentence (or text) simplification and text summarisation. This book offers an overview of the fundamentals of neural models for text production. In particular we elaborate on three main aspects of neural approaches to text production: how sequential decoders learn to generate adequate text how encoders learn to produce better input representations and how neural generators account for task specific objectives. Indeed each text production task raises a slightly different challenge (e.g how to take the dialogue context into account when producing a dialogue turn how to detect and merge relevant information when summarising a text or how to produce a well formed text that correctly captures the information contained in some input data in the case of data to text generation). We outline the constraints specific to some of these tasks and examine how existing neural models account for them. More generally this book considers text to text meaning to text and data to text transformations. It aims to provide the audience with a basic knowledge of neural approaches to text production and a roadmap to get them started with the related work. The book is mainly targeted at researchers graduate students and industrials interested in text production from different forms of inputs.