Major news media frequently uses the method of news timeline summarization to summarize important daily news over major events across the timeline. While various sophisticated methods have been proposed to generate both concise and complete news timelines, in practice, generating timelines from a large number of news articles not only faces quality issues but also encounters the challenge of generation speed, which all existing methods have neglected. To mitigate these issues, in this work, we propose to speed up timeline generation by dividing the whole summarization task into sub-summarization tasks, adopting the “divide and conquer" philosophy: (1) date selection and (2) text summarization. Furthermore, since existing methods in news timeline summarization pay less attention to the date selection than text summarization, in this paper, we re-examine the role of date selection in news timeline summarization and demonstrate that accurate date selection “alone" can significantly contribute to the task of news timeline summarization. Leveraging on the explicit date selection, then, we propose a simple yet fast and effective news timeline summarization method, named WILSON (neWs tImeLine SummarizatiON). Experimented on two widely used timeline summarization benchmark datasets, timeline17 and crisis, empirical evaluation shows that WILSON outperforms state-of-the-art approaches in both speed and ROUGE scores, significantly improving ROUGE-2 F1 scores by 9.5%∼17.7% and reducing generation time by two orders of magnitude. A further user study with professional journalists also validates the superiority of WILSON. Finally, we build a real-time news timeline summarization system and achieve encouraging results on an industrial-level corpus.