  Table-to-text generation refers to generating a descriptive text from a
key-value table. Traditional autoregressive methods, though can generate text
with high fluency, suffer from low coverage and poor faithfulness problems. To
mitigate these problems, we propose a novel Skeleton-based two-stage method
that combines both Autoregressive and Non-Autoregressive generations (SANA).
Our approach includes: (1) skeleton generation with an autoregressive pointer
network to select key tokens from the source table; (2) edit-based
non-autoregressive generation model to produce texts via iterative insertion
and deletion operations. By integrating hard constraints from the skeleton, the
non-autoregressive model improves the generation's coverage over the source
table and thus enhances its faithfulness. We conduct automatic and human
evaluations on both WikiPerson and WikiBio datasets. Experimental results
demonstrate that our method outperforms the previous state-of-the-art methods
in both automatic and human evaluation, especially on coverage and
faithfulness. In particular, we achieve PARENT-T recall of 99.47 in WikiPerson,
improving over the existing best results by more than 10 points.
