We propose a data-to-text generation model with two modules, one for tracking
and the other for text generation. Our tracking module selects and keeps track
of salient information and memorizes which record has been mentioned. Our
generation module generates a summary conditioned on the state of tracking
module. Our model is considered to simulate the human-like writing process that
gradually selects the information by determining the intermediate variables
while writing the summary. In addition, we also explore the effectiveness of
the writer information for generation. Experimental results show that our model
outperforms existing models in all evaluation metrics even without writer
information. Incorporating writer information further improves the performance,
contributing to content planning and surface realization.

本文提出一种数据到文本生成模型，包含追踪模块和文本生成模块，能模拟人类的写作过程，在不需要作者信息的情况下就能在所有的评估度量中表现优异，结合作者信息可以进一步提高性能。