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

Yao Lu, Yue Dong, Laurent Charlin


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.
Anthology ID:
2020.emnlp-main.648
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
8068–8074
Language:
URL:
https://aclanthology.org/2020.emnlp-main.648
DOI:
10.18653/v1/2020.emnlp-main.648
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2020.emnlp-main.648.pdf
Video:
 https://slideslive.com/38938728
Code
 yaolu/Multi-XScience
Data
Multi-XScienceWikiSum