Summarising Historical Text in Modern Languages

• URL: http://arxiv.org/abs/2101.10759v2
• Date: Wed, 27 Jan 2021 04:17:02 GMT
• Title: Summarising Historical Text in Modern Languages
• Authors: Xutan Peng, Yi Zheng, Chenghua Lin, Advaith Siddharthan
• Abstract summary: 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.
• Score: 13.886432536330805
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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