  We introduce the task of historical text summarisation, where documents in
historical forms of a language are summarised in the corresponding modern
language. This is a fundamentally important routine to historians and digital
humanities researchers but has never been automated. We compile a high-quality
gold-standard text summarisation dataset, which consists of historical German
and Chinese news from hundreds of years ago summarised in modern German or
Chinese. Based on cross-lingual transfer learning techniques, we propose a
summarisation model that can be trained even with no cross-lingual (historical
to modern) parallel data, and further benchmark it against state-of-the-art
algorithms. We report automatic and human evaluations that distinguish the
historic to modern language summarisation task from standard cross-lingual
summarisation (i.e., modern to modern language), highlight the distinctness and
value of our dataset, and demonstrate that our transfer learning approach
outperforms standard cross-lingual benchmarks on this task.
