Facilitating news consumption at scale is still quite challenging. Some research e ort focused on coming up with useful structures for facilitating news navigation for humans, but benchmarks and objective evaluation of such structures is not common. One area that has progressed recently is news timeline summarisation. In this talk, we present some of our work on longrange large-scale news timeline summarisation. Timelines present the most important events of a topic linearly in chronological order and are commonly used by news editors to organise long-ranging topics for news consumers. Tools for automatic timeline summarisation can address the cost of manual e ort and the infeasibility of manually covering many topics, over long time periods and massive news corpora. In this talk, we rst compare di erent high-level approaches to timeline summarisation, identify the modules and features important for this task, and present new state-of-the-art results with a simple new method. We provide several examples of automatic timelines and present both a quantitative and qualitative analysis of these structured news summaries. Most of our tools and datasets are available online on github.