Learning to Select, Track, and Generate for Data-to-Text

Abstract

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 remembers which record has been mentioned. Our generation module generates a summary conditioned on the state of tracking module. 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.