Abstract
We introduce LR-Sum, a new permissively-licensed dataset created with the goal of enabling further research in automatic summarization for less-resourced languages.LR-Sum contains human-written summaries for 40 languages, many of which are less-resourced. We describe our process for extracting and filtering the dataset from the Multilingual Open Text corpus (Palen-Michel et al., 2022).The source data is public domain newswire collected from from Voice of America websites, and LR-Sum is released under a Creative Commons license (CC BY 4.0), making it one of the most openly-licensed multilingual summarization datasets.We describe abstractive and extractive summarization experiments to establish baselines and discuss the limitations of this dataset.- Anthology ID:
- 2023.findings-acl.427
- Volume:
- Findings of the Association for Computational Linguistics: ACL 2023
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 6829–6844
- Language:
- URL:
- https://aclanthology.org/2023.findings-acl.427
- DOI:
- Cite (ACL):
- Chester Palen-Michel and Constantine Lignos. 2023. LR-Sum: Summarization for Less-Resourced Languages. In Findings of the Association for Computational Linguistics: ACL 2023, pages 6829–6844, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- LR-Sum: Summarization for Less-Resourced Languages (Palen-Michel & Lignos, Findings 2023)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/2023.findings-acl.427.pdf