Models and Datasets for Cross-Lingual Summarisation
This provides a dataset for researchers working on cross-lingual summarization, but it is incremental as it builds on existing methods and data sources.
The authors tackled the problem of cross-lingual summarization by creating a new corpus with long documents and multi-sentence summaries across twelve language pairs, derived from Wikipedia, and validated it with human studies and experiments showing utility in various scenarios.
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.