  Timeline summarization (TLS) involves creating summaries of long-running
events using dated summaries from numerous news articles. However, limited data
availability has significantly slowed down the development of timeline
summarization. In this paper, we introduce the CNTLS dataset, a versatile
resource for Chinese timeline summarization. CNTLS encompasses 77 real-life
topics, each with 2524 documents and summarizes nearly 60\% days duration
compression on average all topics.
  We meticulously analyze the corpus using well-known metrics, focusing on the
style of the summaries and the complexity of the summarization task.
Specifically, we evaluate the performance of various extractive and generative
summarization systems on the CNTLS corpus to provide benchmarks and support
further research. To the best of our knowledge, CNTLS is the first Chinese
timeline summarization dataset. The dataset and source code are
released\footnote{Code and data available at:
\emph{\url{https://github.com/OpenSUM/CNTLS}}.}.
