SNG-TE: Sports News Generation with Text-Editing Model

Abstract

Currently, the amount of sports news is increasing, given the number of sports available. As a result, manually writing sports news requires high labor costs to achieve the intended efficiency. Therefore, it is necessary to develop the automatic generation of sports news. Most available news generation methods mainly rely on real-time commentary sentences, which have the following limitations: (1) unable to select suitable commentary sentences for news generation, and (2) the generated sports news could not accurately describe game events. Therefore, this study proposes a sports news generation with text-editing model (SNG-TE) is proposed to generate sports news, which includes selector and rewriter modules. Within the study context, a weight adjustment mechanism in the selector module is designed to improve the hit rate of important sentences. Furthermore, the text-editing model is introduced in the rewriter module to ensure that the generated news sentences can correctly describe the game events. The annotation and generation experiments are designed to evaluate the developed model. The study results have shown that in the annotation experiment, the accuracy of the sentence annotated by the selector increased by about 8% compared with other methods. Moreover, in the generation experiment, the sports news generated by the rewriter achieved a 49.66 ROUGE-1 score and 21.47 ROUGE-2, both of which are better than the available models. Additionally, the proposed model saved about 15 times the consumption of time. Hence, the proposed model provides better performance in both accuracy and efficiency, which is very suitable for the automatic generation of sports news.