Abstract
We present a cross-lingual summarisation corpus with long documents in a source language associated with multi-sentence summaries in a target language. The corpus covers twelve language pairs and directions for four European languages, namely Czech, English, French and German, and the methodology for its creation can be applied to several other languages. We derive cross-lingual document-summary instances from Wikipedia by combining lead paragraphs and articles’ bodies from language aligned Wikipedia titles. We analyse the proposed cross-lingual summarisation task with automatic metrics and validate it with a human study. To illustrate the utility of our dataset we report experiments with multi-lingual pre-trained models in supervised, zero- and few-shot, and out-of-domain scenarios.- Anthology ID:
- 2021.emnlp-main.742
- Volume:
- Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- Month:
- November
- Year:
- 2021
- Address:
- Online and Punta Cana, Dominican Republic
- Editors:
- Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 9408–9423
- Language:
- URL:
- https://aclanthology.org/2021.emnlp-main.742
- DOI:
- 10.18653/v1/2021.emnlp-main.742
- Cite (ACL):
- Laura Perez-Beltrachini and Mirella Lapata. 2021. Models and Datasets for Cross-Lingual Summarisation. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 9408–9423, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- Models and Datasets for Cross-Lingual Summarisation (Perez-Beltrachini & Lapata, EMNLP 2021)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2021.emnlp-main.742.pdf
- Code
- lauhaide/clads
- Data
- WikiLingua