  Multi-document summarization is a challenging task for which there exists
little large-scale datasets. We propose Multi-XScience, a large-scale
multi-document summarization dataset created from scientific articles.
Multi-XScience introduces a challenging multi-document summarization task:
writing the related-work section of a paper based on its abstract and the
articles it references. Our work is inspired by extreme summarization, a
dataset construction protocol that favours abstractive modeling approaches.
Descriptive statistics and empirical results---using several state-of-the-art
models trained on the Multi-XScience dataset---reveal that Multi-XScience is
well suited for abstractive models.
