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.

This paper reports a tele-presence robot named ChiCaRo, which is designed for interaction with babies and toddlers. ChiCaRo can physically interact with babies and toddlers by moving around and using its small hand. We conducted a field trial at a playroom where babies and toddlers can freely play to investigate ChiCaRo's effectiveness. In the experiment adult participants interacted with their babies and toddlers by ChiCaRo and another robot. The adult participants evaluated ChiCaRo highly in the context of remote interaction with their babies and toddlers.