  Sports game summarization aims at generating sports news from live
commentaries. However, existing datasets are all constructed through automated
collection and cleaning processes, resulting in a lot of noise. Besides,
current works neglect the knowledge gap between live commentaries and sports
news, which limits the performance of sports game summarization. In this paper,
we introduce K-SportsSum, a new dataset with two characteristics: (1)
K-SportsSum collects a large amount of data from massive games. It has 7,854
commentary-news pairs. To improve the quality, K-SportsSum employs a manual
cleaning process; (2) Different from existing datasets, to narrow the knowledge
gap, K-SportsSum further provides a large-scale knowledge corpus that contains
the information of 523 sports teams and 14,724 sports players. Additionally, we
also introduce a knowledge-enhanced summarizer that utilizes both live
commentaries and the knowledge to generate sports news. Extensive experiments
on K-SportsSum and SportsSum datasets show that our model achieves new
state-of-the-art performances. Qualitative analysis and human study further
verify that our model generates more informative sports news.

Computation and Language;Artificial Intelligence