Many ML studies have introduced systems for deciphering and translating ancient texts into modern language, and these have proven useful to history, archaeology and digital humanities scholars. Now, researchers from the University of Sheffield, Beihang University, and Open University's Knowledge Media Institute have proposed a transfer learning approach that can automatically process historical texts at a semantic level to generate modern language summaries. The method outperforms standard cross-lingual benchmarks on the task. Historical text summarization can be regarded as a unique form of cross-lingual summarization. Progress in traditional cross-lingual summarization has however been hindered by limited historical and modern language corpora and evolving vocabulary, spelling, meanings and grammar.