Models and Datasets for Cross-Lingual Summarisation

Laura Perez-Beltrachini, Mirella Lapata


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
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
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.emnlp-main.742.pdf
Code
 lauhaide/clads
Data
WikiLingua