Text-to-Text Generation -- Probabilistic Approaches for Modeling Text Structure and Their Application to Text-to-Text Generation -- Spanning Tree Approaches for Statistical Sentence Generation -- On the Limits of Sentence Compression by Deletion -- NLG in Interaction -- Learning Adaptive Referring Expression Generation Policies for Spoken Dialogue Systems -- Modelling and Evaluation of Lexical and Syntactic Alignment with a Priming-Based Microplanner -- Natural Language Generation as Planning under Uncertainty for Spoken Dialogue Systems -- Referring Expression Generation -- Generating Approximate Geographic Descriptions -- A Flexible Approach to Class-Based Ordering of Prenominal Modifiers -- Attribute-Centric Referring Expression Generation -- Evaluation of NLG -- Assessing the Trade-Off between System Building Cost and Output Quality in Data-to-Text Generation -- Human Evaluation of a German Surface Realisation Ranker -- Structural Features for Predicting the Linguistic Quality of Text -- Towards Empirical Evaluation of Affective Tactical NLG -- Shared Task Challenges for NLG -- Introducing Shared Tasks to NLG: The TUNA Shared Task Evaluation Challenges -- Generating Referring Expressions in Context: The GREC Task Evaluation Challenges -- The First Challenge on Generating Instructions in Virtual Environments.

Natural language generation (NLG) is a subfield of natural language processing (NLP) that is often characterized as the study of automatically converting non-linguistic representations (e.g., from databases or other knowledge sources) into coherent natural language text. In recent years the field has evolved substantially. Perhaps the most important new development is the current emphasis on data-oriented methods and empirical evaluation. Progress in related areas such as machine translation, dialogue system design and automatic text summarization and the resulting awareness of the importance of language generation, the increasing availability of suitable corpora in recent years, and the organization of shared tasks for NLG, where different teams of researchers develop and evaluate their algorithms on a shared, held out data set have had a considerable impact on the field, and this book offers the first comprehensive overview of recent empirically oriented NLG research.