Models and Datasets for Cross-Lingual Summarisation

By Laura Perez-Beltrachini, Mirella Lapata. 2022

57 citations recorded by the source corpus.

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.

Subfields: Natural Language Processing

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Citations in the corpus

Repairing the Cracked Foundation: A Survey of Obstacles in Evaluation Practices for Generated Text (2022, background): “Additionally, only two of the 27 datasets were nonEnglish, despite much recent work that introduces multilingual summarization corpora (Giannakopoulos et al., 2015; Scialom et al., 2020; Ladhak et al., 2020; Hasan et al., 2021; Perez-Beltrachini and Lapata, 2021).”