  Table-to-text generation aims at automatically generating natural text to
help people to conveniently obtain the important information in tables.
Although neural models for table-to-text have achieved remarkable progress,
some problems still overlooked. The first is that the values recorded in many
tables are mostly numbers in practice. The existing approaches do not do
special treatment for these, and still regard these as words in natural
language text. Secondly, the target texts in training dataset may contain
redundant information or facts do not exist in the input tables. These may give
wrong supervision signals to some methods based on content selection and
planning and auxiliary supervision. To solve these problems, we propose two
self-supervised tasks, Number Ordering and Significance Ordering, to help to
learn better table representation. The former works on the column dimension to
help to incorporate the size property of numbers into table representation. The
latter acts on row dimension and help to learn a significance-aware table
representation. We test our methods on the widely used dataset ROTOWIRE which
consists of NBA game statistic and related news. The experimental results
demonstrate that the model trained together with these two self-supervised
tasks can generate text that contains more salient and well-organized facts,
even without modeling context selection and planning. And we achieve the
state-of-the-art performance on automatic metrics.
