  In the rapidly evolving digital era, there is an increasing demand for
concise information as individuals seek to distil key insights from various
sources. Recent attention from researchers on Multi-document Summarisation
(MDS) has resulted in diverse datasets covering customer reviews, academic
papers, medical and legal documents, and news articles. However, the
English-centric nature of these datasets has created a conspicuous void for
multilingual datasets in today's globalised digital landscape, where linguistic
diversity is celebrated. Media platforms such as British Broadcasting
Corporation (BBC) have disseminated news in 20+ languages for decades. With
only 380 million people speaking English natively as their first language,
accounting for less than 5% of the global population, the vast majority
primarily relies on other languages. These facts underscore the need for
inclusivity in MDS research, utilising resources from diverse languages.
Recognising this gap, we present the Multilingual Dataset for Multi-document
Summarisation (M2DS), which, to the best of our knowledge, is the first dataset
of its kind. It includes document-summary pairs in five languages from BBC
articles published during the 2010-2023 period. This paper introduces M2DS,
emphasising its unique multilingual aspect, and includes baseline scores from
state-of-the-art MDS models evaluated on our dataset.
