Using GAN to generate sport news from live game stats

Abstract

One goal in artificial intelligence field is to create well-formed and human-like natural language text given data input and a specific goal. Some data-to-text solutions have been proposed and successfully used in real applied domains. Our work focuses on a new domain, Automatic Sport News Generating, which aims to produce sport news immediately after each match is over so that both time and labor can be saved on writing the news articles. We propose to use Generative Adversarial Networks (GAN) architecture for generating sport news based on game stats. Our model can automatically determine what is worth reporting and generate various appropriate descriptions about the game. We apply our approach to generate NBA (National Basketball Association) game news. Especially, This paper focuses on reporting the summary of game result and performance of players. Our model achieves good results on both tasks. To our best knowledge, this is the first work based on GAN to generate sports news using game statistics.