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
- Editors:
- Bonnie Webber, Trevor Cohn, Yulan He, Yang Liu
- 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
- Cite (ACL):
- Yao Lu, Yue Dong, and Laurent Charlin. 2020. Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 8068–8074, Online. Association for Computational Linguistics.
- Cite (Informal):
- Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles (Lu et al., EMNLP 2020)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2020.emnlp-main.648.pdf
- Code
- yaolu/Multi-XScience
- Data
- Multi-XScience, Microsoft Academic Graph, WikiSum