LR-Sum: Summarization for Less-Resourced Languages
Chester Palen-Michel, Constantine Lignos
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.
BibTeX
@inproceedings{palen-michel-lignos-2023-lr,
title = "{LR}-Sum: Summarization for Less-Resourced Languages",
author = "Palen-Michel, Chester and
Lignos, Constantine",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-acl.427/",
doi = "10.18653/v1/2023.findings-acl.427",
pages = "6829--6844"
}