LR-Sum: Summarization for Less-Resourced Languages
This work addresses the problem of limited data availability for researchers working on summarization in less-resourced languages, though it is incremental as it builds on existing multilingual corpora.
The authors tackled the lack of datasets for automatic summarization in less-resourced languages by creating LR-Sum, a permissively-licensed dataset with human-written summaries for 40 languages, many of which are less-resourced, derived from public domain newswire sources.
This preprint describes work in progress on 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 how we plan to use the data for modeling experiments and discuss limitations of the dataset.