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Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles

Yao Lu,Yue Dong,Laurent Charlin

2020 · DOI: 10.18653/v1/2020.emnlp-main.648
Conference on Empirical Methods in Natural Language Processing · 145 Citations

TLDR

This work proposes Multi-XScience, a large-scale multi-document summarization dataset created from scientific articles, inspired by extreme summarization, a dataset construction protocol that favours abstractive modeling approaches.

Abstract

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.