Most people do not interact with Semantic Web data directly. Unless they have the expertise to understand the underlying technology, they need textual or visual interfaces to help them make sense of it. We explore the problem of generating natural language summaries for Semantic Web data. This is non-trivial, especially in an open-domain context. To address this problem, we explore the use of neural networks. Our system encodes the information from a set of triples into a vector of fixed dimensionality and generates a textual summary by conditioning the output on the encoded vector. We train and evaluate our models on two corpora of loosely aligned Wikipedia snippets and DBpedia and Wikidata triples with promising results.
@cite_27: Figures Preface 1. Introduction 2. National Language Generation in practice 3. The architecture of a Natural Language Generation system 4. Document planning 5. Microplanning 6. Surface realisation 7. Beyond text generation Appendix References Index.
Models for NLG can be divided into two groups: statistical and rule-based ones @cite_27 . The latter employ linguistic expertise and work in three different phases:. During document planning the information that will be communicated in the text is selected and organised (i.e. document structuring). The output of the document planner is used by the microplanner to decide how this information should be linguistically expressed in the generated text. Subsequently, the realiser generates the actual text by applying a specific template that satisfies the linguistic requirements that are set by the microplanner, and expresses the information as it is structured by the document planner. Each one of the above mentioned stages is associated almost explicitly not only with the domain of the end-application but, in most cases, with the application itself.