LR -Sum: Summarization for Less-Resourced Languages (2023)

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.

## introduction datasets for automatic summarization have historically focused largely on english, and while there has recently been a greater focus on datasets that include other languages @xcite @xcite @xcite , there still remains a need for high-quality summarization data for lessresourced languages. datasets with human-written summaries are important for both training statistical summarization models and for automatic evaluation of them. while recently there have been a growing number of multilingual summarization datasets, many are relatively small, have limited language coverage, have restrictive licenses, or a combination of these drawbacks. in this paper, we present lr-sum, a new 40language summarization dataset with a focus on less-resourced languages. 1 we created it with the summary: fuad huseyîn li civîna ny jiber êrîşê enqere tawanbar kir; daxwaza tazmînat û lêpirsîneke navneteweyî kir. article: wezîrê derve yê îraqê fuad huseyîn doh sêşemê li civîna awarte ya civata ewlekarîyê ya neteweyên yekbûyî (ny), daxwaza vekişîna hêzên tirkîyê ji axa îraqê kir. "em hebûna neqanûnî ya hêzên artêşa tirkîyê li ser axa îraqê şermezar dikin," huseyîn got. civata ewlekarîyê ya neteweyên yekbûyî (ny) ser daxwaza îraqê kom bû, di derbarê êrîşa hefteya borî ya li duhokê hat kirin û di encamê de 9 kes hatibûn kuştin û 23 kesên din jî birîndar bûbûn. îraq jiber êrîşa kujer hêzên tirkiyê tawanbar dike û wezîrê derve huseyîn çû new yorkê da ku beşdarî civîna awarte ya ny bibe. fuad huseyîn li civînê ser navê bexdayê, jiber êrîşê enqere tawanbar kir û daxwaza tazmînatê û lêpirsîneke navneteweyî kir. @xcite tables 1 and 2 show example article-summary pairs from lr-sum and highlight how similar content in the summary is not merely simple extraction from the text. results of experiments described in section 4 show that for many less-resourced languages, the task of producing summaries remains challenging, enabling lr-sum to serve as a benchmark of progress. lr-sum is released via github at https://github.com/bltlab/lr-sum . summary: first-ever aerial census will be conducted simultaneously across five states to determine elephant migration patterns and numbers article: five southern african countries, with more than half the continent's elephants, are conducting a first-ever aerial census to determine the elephant population and how to protect it. light aircraft will fly simultaneously across the plains of angola, botswana, namibia, zambia and zimbabwe -in a conservation area known as the kavango-zambezi trans-frontier conservation area (kaza) -in an exercise that will run until october 20. [...] we hope to see what the results come up with," ives said. "what we will be interested in seeing is not only how many elephants there are but the distribution, therefore, and what the likelihood of those elephants moving between countries is. ## related work in this section, we briefly list existing english and multilingual summarization datasets and discuss work in dataset creation for less-resourced languages more generally. ## results and discussion overall, we find that abstractive models fail to beat extractive ones for some languages, while extractive models and even the lead-3 baseline remain competitive for others. the fact that the baselines and extractive approaches still outperform abstractive neural models demonstrates the potential use of this corpus for further summarization research to improve abstractive models in less-resourced settings. results comparing the different approaches for 12 languages are shown in ## conclusions and future work we have presented lr-sum, a permissivelylicensed summarization dataset for less-resourced languages based on news data. we have demonstrated lr-sum's usefulness in augmenting the training data of other multilingual summarization models and demonstrated potential for further research in summarization for less-resourced languages. even with the best performing model, the results are only slightly higher than the lead-3 baseline, which indicates ample room for improvement and future research directions. in future work, we plan to experiment with leveraging additional training data like the remaining portions of the mot data which were not suitable for extracting summaries but may still be useful in fine-tuning a multilingual language model to perform better on certain less-resourced languages. lr-sum also presents opportunities for few-and zero-shot experimentation for languages where there are not enough examples to use as training data, but where the data that does exist may be useful as a test set. we look forward to collaborating with speakers of the languages included in lr-sum to further increase the quality and quantity of summarization data for less-resourced languages.