Summarising Historical Text in Modern Languages

Xutan Peng, Yi Zheng, Chenghua Lin, Advaith Siddharthan


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.
Anthology ID:
2021.eacl-main.273
Volume:
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
Month:
April
Year:
2021
Address:
Online
Venue:
EACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3123–3142
Language:
URL:
https://aclanthology.org/2021.eacl-main.273
DOI:
10.18653/v1/2021.eacl-main.273
Bibkey:
Cite (ACL):
Xutan Peng, Yi Zheng, Chenghua Lin, and Advaith Siddharthan. 2021. Summarising Historical Text in Modern Languages. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 3123–3142, Online. Association for Computational Linguistics.
Cite (Informal):
Summarising Historical Text in Modern Languages (Peng et al., EACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.eacl-main.273.pdf
Code
 Pzoom522/HistSumm
Data
LCSTSMLSUM