  Generating texts from structured data (e.g., a table) is important for
various natural language processing tasks such as question answering and dialog
systems. In recent studies, researchers use neural language models and
encoder-decoder frameworks for table-to-text generation. However, these neural
network-based approaches do not model the order of contents during text
generation. When a human writes a summary based on a given table, he or she
would probably consider the content order before wording. In a biography, for
example, the nationality of a person is typically mentioned before occupation
in a biography. In this paper, we propose an order-planning text generation
model to capture the relationship between different fields and use such
relationship to make the generated text more fluent and smooth. We conducted
experiments on the WikiBio dataset and achieve significantly higher performance
than previous methods in terms of BLEU, ROUGE, and NIST scores.
