  Recent neural models have shown significant progress on the problem of
generating short descriptive texts conditioned on a small number of database
records. In this work, we suggest a slightly more difficult data-to-text
generation task, and investigate how effective current approaches are on this
task. In particular, we introduce a new, large-scale corpus of data records
paired with descriptive documents, propose a series of extractive evaluation
methods for analyzing performance, and obtain baseline results using current
neural generation methods. Experiments show that these models produce fluent
text, but fail to convincingly approximate human-generated documents. Moreover,
even templated baselines exceed the performance of these neural models on some
metrics, though copy- and reconstruction-based extensions lead to noticeable
improvements.
