  Neural data-to-text generation models have achieved significant advancement
in recent years. However, these models have two shortcomings: the generated
texts tend to miss some vital information, and they often generate descriptions
that are not consistent with the structured input data. To alleviate these
problems, we propose a Neural data-to-text generation model with Dynamic
content Planning, named NDP for abbreviation. The NDP can utilize the
previously generated text to dynamically select the appropriate entry from the
given structured data. We further design a reconstruction mechanism with a
novel objective function that can reconstruct the whole entry of the used data
sequentially from the hidden states of the decoder, which aids the accuracy of
the generated text. Empirical results show that the NDP achieves superior
performance over the state-of-the-art on ROTOWIRE dataset, in terms of relation
generation (RG), content selection (CS), content ordering (CO) and BLEU
metrics. The human evaluation result shows that the texts generated by the
proposed NDP are better than the corresponding ones generated by NCP in most of
time. And using the proposed reconstruction mechanism, the fidelity of the
generated text can be further improved significantly.
