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.

我们引入历史文本摘要的任务，其中历史形式的语言文档被概括为相应的现代语言。基于跨语言迁移学习技术，我们提出了一个摘要模型，可以即使没有跨语言（历史到现代）平行数据，也可以进行训练，并对最先进的算法进行基准测试。我们报告了自动和人工评估，突出了我们数据集的独特性和价值，并证明了我们的迁移学习方法在这项任务上优于标准的跨语言基准。