Generating Topic-Preserving Synthetic News
Ahmadreza Mosallanezhad, Kai Shu, Huan Liu · 2021 IEEE International Conference on Big Data (Big Data) · 2021
The text generation methods have witnessed great success in text summarization, machine translation, and synthetic news generation. However, these techniques may be abused to generate disinformation and fake news. To better understand the potential threats of synthetic news, we develop a novel generation method RLTG to generate topic-preserving news content. The majority of existing text generation methods are either controlled by specific attributes or lack topic consistency between the input claims and output news, making synthetic news less coherent and realistic. In this paper, we study the problem of topic-preserving synthetic news generation by proposing a novel deep reinforcement learning-based method to control the output of large pre-trained language models. Experiment results on real-world datasets demonstrate that the news contents generated by RLTG are topic-consistent and realistic.