LR-Sum: Summarization for Less-Resourced Languages
Year: 2023

Abstract:
We introduce LR-Sum, a new permissivelylicensed 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 openlylicensed multilingual summarization datasets. We describe abstractive and extractive summarization experiments to establish baselines and discuss the limitations of this dataset.

heading: 1 Introduction
text: 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 (Cao et al., 2020; Giannakopoulos et al., 2015, 2017; Hasan et al., 2021; Scialom et al., 2020), 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 40- language summarization dataset with a focus on less-resourced languages.1 We created it with the
1There is no definitive definition for less-resourced (Liu et al., 2022) and we take the view that less-resourced can depend on the intersection of many factors (Lignos et al., 2022), including what task a dataset is created for.
goal of providing high-quality, human-written summaries in as many languages as possible. The collection of curated and filtered summaries that comprise LR-Sum are licensed using a Creative Commons Attribution license (CC BY 4.0), and the articles that it was collected from are in the public domain. This allows LR-Sum to be distributed freely and annotated without restriction, unlike many summarization datasets which use copyrighted material, often redistributed without appropriate licensing. For many of the languages in LR-Sum, this is the largest collection of summarization data with such a permissive license.
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.
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heading: 2 Related Work
text: In this section, we briefly list existing English and multilingual summarization datasets and discuss work in dataset creation for less-resourced languages more generally.

heading: 2.1 English Summarization Datasets
text: Document Understanding Conference (DUC)2 (Harman and Over, 2004; Dang, 2006) create English summarization datasets for evaluations.
The NYT Annotated Corpus (Sandhaus, 2008) is a corpus of New York Times articles and 600k summaries written by library scientists.
CNN/Daily Mail (Hermann et al., 2015) was originally created for question answering, but Nallapati et al. (2016) adapt this dataset for summarization.
XSum (Narayan et al., 2018a) uses the first sentence “story body introduction” tag of a BBC article as the summary and the remainder of the text as the article and show that XSum favors abstractive summaries.

heading: 2.2 Multilingual Summarization Datasets
text: MLSUM (Scialom et al., 2020) is an extension of the CNN/Daily Mail dataset for five languages: French, German, Spanish, Turkish, and Russian.
MultiLing (Giannakopoulos et al., 2015, 2017) is a shared task that focuses on multilingual summarization covering upwards of 40 languages, but the dataset size is somewhat limited, with training sets of only around 10,000 articles in total.
2http://duc.nist.gov/
XL-Sum (Hasan et al., 2021) includes 44 languages, many of which are less-resourced languages, by scraping BBC News and making use of bullet points as summaries. XL-Sum has a more restrictive license than LR-Sum.
MassiveSumm (Varab and Schluter, 2021) is a very large web-scraped summarization corpus that covers the majority of languages covered both in our dataset, LR-Sum, and also XL-Sum, and it does so in larger quantities. However, MassiveSumm cannot be easily redistributed due to copyright and being scraped from various news sites. MassiveSumm’s GitHub README contains the disclaimer “The data is noisy and recall-oriented.”3
MultiSumm (Cao et al., 2020) creates summaries from titles for Bosnian and Croatian.

heading: 2.3 Data for Less-Resourced Languages
text: A number of other text corpora have been created for less-resourced languages for summarization and other tasks. Abdulrahman et al. (2019) create a Kurdish corpus of textbooks. Vasili et al. (2018) conduct a study on summarization of Albanian and build a small dataset. Malajyan et al. (2020) create a corpus of paraphrases in Armenian. Niyongabo et al. (2020) create a corpus for classification of news in Kinyarwanda and Kirundi. Azime and Mohammed (2021) create a news dataset in Amharic. Marivate et al. (2020) investigate corpus creation for Setswana and Sepedi. Koto et al. (2020) create a summarization corpus for Indonesian with over 200k articles, but it has significant license restrictions. Das and Bandyopadhyay (2010) create a system for opinion article summarization for Bangla. Nguyen et al. (2020) create a dataset and experiment with sentence compression in Vietnamese. Birhanu (2017) create an extractive summarization system and evaluate on a small dataset in Tigrinya. Jaruskulchai and Kruengkrai (2003) create an extractive model for Thai, and Chumpolsathien (2020) create a large-scale dataset for Thai summarization. Buoy et al. (2021) explore text classification with Khmer.
Multilingual Open Text (MOT) (Palen-Michel et al., 2022) is a corpus collected from the websites of Voice of America, an international news service funded by the U.S. Government providing news articles and other short snippets like audio and image descriptions. Our work creates a summarization dataset for the majority of the lan-
3https://github.com/danielvarab/massive-summ
guages within MOT. MOT has a permissive license (CC BY 4.0), and the original source articles are in the public domain. By comparison, many of the multilingual datasets derived from privately funded news sources like CNN or BBC News were collected from copyrighted data without the copyright owner’s permission, limiting legal distribution. XL-Sum’s license is CC BY-NC-SA 4.0, which restricts commercial usage. MOT contains news text data for many less-resourced languages, some of which overlap with XL-Sum and some of which are complementary. We discuss which languages are present in LR-Sum vs XL-Sum in more detail in Section 3.2.

heading: 3 LR-Sum: Dataset

heading: 3.1 Methodology
text: The approach for creating LR-Sum is to leverage the coverage of less-resourced languages in MOT to construct a summarization dataset. MOT (PalenMichel et al., 2022) semi-regularly releases new versions of the dataset as new articles are published on Voice of America’s website. We use MOT release v1.6 from October 1, 2022 for the creation of LR-Sum. Only the content type of “article” is included in LR-Sum since the categories of photo, audio, etc. already tend to be short snippets describing content, which typically are too short to make useful article-summary pairs.
While bold text or bullet points are used in some other summarization datasets (Hasan et al., 2021; Hermann et al., 2015; Narayan et al., 2018a), these ways of extracting summaries are not available in VOA articles. Instead a description field is present for VOA articles. This description field in VOA new articles can be noisy. While it is generally used to give a brief summary of the article contents, there are numerous instances where the description contains the first few lines of the article, information about the authors, or general information about what VOA is.
A number of filtering steps are taken to ensure high-quality summaries. First, we filter to ensure that the description field has content and that the content of the description field is at least 10 tokens long.4 Then, we filter out any articles that do not have a minimum of 10 sentences. We also filter by total number of tokens to remove outlier articles with fewer than 30 or more than 6,000 tokens.
4All tokenization comes from the tokenizers used in the creation of the MOT corpus.
When an article does not have a human-written summary, the description field simply contains the first few sentences. Because ellipses can signal that the description is just a copy of the first few sentences of the article, we also filter out all descriptions that end with ellipses. We further remove these instances from the dataset by limiting token overlap of the description and the first 3 sentences to 85%.5 With the goal of keeping LR-Sum from being purely extractive, we also block descriptions where an oracle extractive approach selecting the best sentence in the article produces a ROUGE-2 score above 95.
We manually created a list of 254 sentences to remove from summaries based on strings that appear the most frequently in the description field. Examples include “Amerikan basınında haftaiçi hergün öne çıkan başlıkları Amerika’nın Sesi’nde bulubilirsiniz” (“You can find the highlights of the American press every weekday on Voice of America” in Turkish) or “Këtë javë në Uashington” (“Live from Washington” in Albanian).6
While MOT includes data in the Lingala and Oromo languages, we do not include them in LRSum since fewer than 100 articles made it through our filtering process. Lingala had only 3 articles, and Oromo 29. MOT also includes data in Bambara, but it contains so few articles that none made it through the filtering process.

heading: 3.2 Dataset Description
text: LR-Sum includes 40 languages in total. We show various statistics of the dataset in Table 3. Figure 1 provides a histogram of article lengths, and Figure 2 provides a histogram of summary lengths.
We measure mean length of articles and summaries in token counts. Compression is 1 - the ratio between summary length and article length as used by Bommasani and Cardie (2020) and Hasan et al. (2021). Mean novelty is the proportion of tokens in the summary that do not occur in the article. LR-Sum’s measures are comparable with MLSUM (Scialom et al., 2020) and XL-Sum (Hasan et al., 2021) for languages shared between datasets. The overall mean article length for LR-Sum is 520.7 and the overall mean summary length is 36.5. For comparison, MLSUM’s English section has a mean article length of 709.2 and mean summary length
5This cutoff was chosen based on manual review of summaries in multiple languages.
6The list of excluded sentences is released with the dataset.
of 55.6, while their Turkish section has mean article length of 309.1 and mean summary length of 22.8.
LR-Sum includes fourteen languages that are not covered by XL-Sum. However, Dari Persian and Kinyarwanda are quite close to Persian Farsi and Kirundi, which are contained in XL-Sum. Seven of the remaining twelve languages have more than 1,000 article-summary pairs for training: Albanian, Bosnian, Khmer, Sorani Kurdish, Lao, Macedonian, and Northern Ndebele. Armenian, Georgian, Haitian Creole, and Shona have fewer than 1,000 training examples. Tibetan and Greek have fewer than 1,000 article-summary pairs overall, which is not enough for training and test splits. Instead, the Tibetan and Greek data could still be useful as a test set for automatic evaluation of models built for those languages or used in few-shot training.
LR-Sum includes languages which can be complementary to existing resources. For example, LR-Sum includes almost twice as many articles in Burmese as XL-Sum. For many languages (i.e. Turkish, Azerbaijani, Persian, Korean) adding LRSum to XL-Sum results in more than double the amount of data available in XL-Sum alone.
LR-Sum also has some unique subdivisions and special focuses for certain languages. Its English section can be subdivided into Zimbabwe and Cambodia-focused sections. Similarly, the French and Portuguese found in LR-Sum tends to be news focused on Africa. Chinese is divided into simplified and traditional varieties. Kurdish is subdivided into the Kurmanji and Sorani dialects. LR-Sum separates Farsi and Dari as separate languages based on their provenance from separate VOA sites, despite their being largely mutually intelligible.

heading: 3.3 Dataset Splits
text: We report the size of the dataset splits for LR-Sum in Appendix 9. Splits are 80% train, 10% validation, and 10% test, except for languages where the number of examples was quite small. To ensure enough test and validation data when possible, in cases where the total was below 4,000 examples, we took 500 for validation and test each and left the rest for training. For languages where the total number of examples was fewer than 1,000, we only created test sets and did not create training or validation data (Amharic, Bangla, Greek, Hausa, Kinyarwanda, Somali, Swahili, Tibetan, and Tigrinya).

heading: 4 Experiments

heading: 4.1 Methodology
text: We conduct three experiments to demonstrate the usefulness of LR-Sum and establish baseline performance on the dataset. For all abstractive models trained, we use mT5 (Xue et al., 2021) as the base model. We report ROUGE-1 and 2 (R1, R2) and ROUGE-L (RL; Lin, 2004) scores.7,8 1. We train individual baseline models for 12 lessresourced languages that are unique to LR-Sum and not present in XL-Sum.9 2. We conduct a series of experiments with extractive models for the less-resourced languages unique to LR-Sum. 3. We train a multilingual model using the concatenation of LR-Sum and XL-Sum training sets and
7We use Hasan et al. (2021)’s adaptation, but for consistency across languages, we do not make use of any of the stemmers available in the reported scores.
8All reported results are from single runs. 9We treat Kurmanji and Sorani varieties of Kurdish separately here since they use different scripts and refer to them as kur-k for Kurmanji and kur-s for Sorani in results tables.
compare with using a multilingual model checkpoint trained on XL-Sum alone. For this experiment, we evaluate both models on LR-Sum’s test sets and evaluate on all less-resourced languages.

heading: 4.1.1 Individual Models
text: We fine-tune 12 models for each of the lessresourced languages not present in XL-Sum. We use mT5 (Xue et al., 2021) as the base model. For these experiments, we use the same training script as Hasan et al. (2021), which is a modified version of a script from the Hugging Face Transformers Library (Wolf et al., 2020). We use the same hyperparameter settings as Hasan et al. (2021). The details of hyper-parameters can be found in Appendix A.1.

heading: 4.1.2 Extractive Baselines
text: We conducted experiments to determine whether extractive approaches might work better given the small training set sizes. Previous work by Nallapati et al. (2017); Narayan et al. (2018b); Zhang et al. (2018) and Scialom et al. (2020), among others, has shown lead-3 (the first three sentences of the article) to be a strong summarization baseline. To demonstrate the strongest possible extractive performance, we also report the oracle, which here is simply selecting the single sentence in the article which produces the highest ROUGE score. We additionally report results for LexRank (Erkan and Radev, 2004) and Luhn (Luhn, 1958) extractive methods. For implementations of these extractive approaches, we used sumy10 (Belica, 2013). The sentence segmentation and tokenizations from the MOT corpus were used for the extractive approaches requiring segmentation and tokenization.

heading: 4.1.3 Multilingual Models
text: Following Hasan et al. (2021)’s reported better performance with multilingual training, we train a multilingual model but instead with the concatenation of the training sets of LR-Sum and XL-Sum. In this experiment we also use the same modified Hugging Face script (Wolf et al., 2020) that Hasan et al. (2021) use for training along with the same hyper-parameters as Hasan et al. (2021) used for multilingual training. Hyper-parameter settings can be found in Appendix A.2.

heading: 5 Results and Discussion
text: 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 Table 4. The multilingual models tend to produce higher scores, likely due to positive transfer between languages. However, the advantage is often only a few points beyond individual or extractive models. The results of combining datasets (Table 7) show how LRSum can be combined with existing summarization datasets like XL-Sum to improve multilingual summarization model coverage. The additional data from the concatenation of LR-Sum and XL-Sum shows an expected advantage for languages not seen by the XL-Sum-only multilingual model.

heading: 5.1 Individual Model Results
text: The results of training the individual models are shown in Tables 4 and 5. The scores are generally slightly lower than the multilingual model with the exception of Albanian, Lao, and Northern Ndebele. The difference in training set size does not appear to be a factor in the performance, potentially because all the training set sizes for these less-resourced languages are small compared to the usual hundreds of thousands of examples found in datasets like MLSUM (Scialom et al., 2020). A language’s presence in mT5’s pre-training also does not appear to be indicative of better performance.

heading: 5.2 Extractive Results
text: The results for extractive models can be found in Table 6. Oracle gives a sense of the upper bound that can be achieved through extractive models. The scores for the oracle are higher than both individual and multilingual abstractive models, which suggests there is plenty of room for improving performance for the abstractive baselines.
For all the languages we evaluated, LexRank had higher scores than Luhn in terms of ROUGE1, though Luhn was slightly higher in ROUGE-2 and ROUGE-L for Haitian Creole, Bosnian and
10https://miso-belica.github.io/sumy/
Albanian. Lead-3 proves to be a strong baseline and scores higher than the extractive models for RL and frequently for R1 and R2. In terms of R1, LexRank outperforms the individual abstractive models for Khmer, Georgian, Bosnian, Northern Ndebele, and Shona but ROUGE-L scores tend to be higher for the individual abstractive models. The multilingual model still beats the lead-3 baseline except for Northern Ndebele and Khmer as shown in Table 4.

heading: 5.3 Multilingual Model Results
text: Table 7 shows the results of mT5 (Xue et al., 2021) trained on the concatenation of training data from LR-Sum and XL-Sum compared with the model
checkpoint of mT5 trained on XL-Sum only. As expected, languages not present in XL-Sum had much better performance with the model trained on both datasets. Dari Persian did not perform better likely due to Farsi already being represented in XL-Sum and the two languages being very similar. Scores for Greek and Tibetan were effectively zero as there is only enough data in LR-Sum for a test set and so there was no training data in those languages due to data scarcity.
The results for additional training data for languages present in both languages are more mixed. Despite both datasets being news data, it is possible there are differences in dialect, topic, or standardization that account for the differences. We discuss the performance of the two multilingual models evaluated on the XL-Sum test set in Appendix B.

heading: 6 Conclusions and Future Work
text: 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 use-
ful 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 LRSum to further increase the quality and quantity of summarization data for less-resourced languages.
Limitations
A limitation of this work is that the dataset has not yet been thoroughly vetted by native speakers of the languages contained in the dataset. We acknowledge the importance of working with native speakers and manually reviewing datasets in greater detail as argued for by Kreutzer et al. (2022) and Lignos et al. (2022). We hope to do more manual review of LR-Sum and other summarization datasets in the near future.
Ethics Statement
Our work provides a dataset for further research on summarization for less-resourced languages. Automatic summarization has the potential to assist users in digesting information. It is our intention that providing a summarization dataset with coverage of less-resourced languages will benefit speakers of languages that may otherwise not have had access to this technology.
However, there is also cause for caution. The results of our work used automatic evaluation metrics and generated summaries have not yet been subjected to more rigorous human review. Even just
based on automated metrics, it is clear there is still room for improvement of the models as they tend to score lower than higher resourced counterparts on similar tasks. Therefore, the models presented in this work should be considered baselines for further work. The dataset and models presented in this work are meant to support further research in summarization of less-resourced languages and not intended for immediate deployment in applications.
In particular, the abstractive summarization models, like most text generation models, have the potential to make factual errors, which have the potential to mislead or misinform. Additionally, both extractive and abstractive models may lack adequate context or miss important information. As mentioned in the limitations section, this dataset, like most summarization news datasets, has not been fully manually reviewed and so may contain a few erroneous summaries despite our best efforts.

heading: Acknowledgments
text: Chester Palen-Michel was supported by a grant from eBay while performing this work.

heading: A Hyper-parameter Settings
text: The hyper-parameters for both individual models and multilingual models were chosen following Hasan et al. (2021).
A.1 Individual Models For hyperparameters in training each individual model we use a learning rate of 5.0e-4, per device train batch size of 2, 16 gradient accumulation steps, 100 warm-up steps, a maximum input length of 512, a maximum inference length of 84, a beam size of 4, no repeat ngram size of 2, length penalty of 0.6, label smoothing factor of 0.1 and weight decay of 0.01. We train for 10 epochs. Training time was roughly 2 days in total to train the 12 individual models.
A.2 Multilingual Model We use a learning rate of 1.0, 5000 warmup steps, weight decay of 0.01, per device train batch size of 2, 16 gradient accumulation steps, maximum steps of 50,000, a label smoothing factor of 0.1, and an upsampling factor of 0.5. We trained on two NVIDIA GeForce RTX 3090 GPUs. Training time was roughly 3 days.

heading: B Evaluating Multilingual Models on XL-Sum
text: We additionally evaluated the two multilingual models on the XL-Sum test data. The results can be seen in Table 8. We found that the addition of LR-Sum data did not have a positive impact on performance but instead tended to degrade model performance slightly. We speculate that despite both being news summarization datasets there could be some amount of difference in content or style that accounts for the slightly lower performance. Another plausible explanation could be that adding relatively small amounts of data for additional languages degrades performance due to the model’s limited capacity to add additional languages.

heading: C Dataset Splits
text: Table 9 shows the dataset splits as described in Section 3.3
ACL 2023 Responsible NLP Checklist

heading: A For every submission:

heading: 3 A1. Did you describe the limitations of your work?
text: Section is unnumbered but is last section. (would be section 7).
7 A2. Did you discuss any potential risks of your work? The risks of this work are no different than the risks of automatic summarization more broadly.

heading: 3 A3. Do the abstract and introduction summarize the paper’s main claims?
text: Left blank.
7 A4. Have you used AI writing assistants when working on this paper? Left blank.
B 3 Did you use or create scientific artifacts? Section 3 and 4

heading: 3 B1. Did you cite the creators of artifacts you used?
text: Section 3 and 4

heading: 3 B2. Did you discuss the license or terms for use and / or distribution of any artifacts?
text: Section 1

heading: 3 B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided
text: that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? Section 1
B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? Not applicable. Left blank.

heading: 3 B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and
text: linguistic phenomena, demographic groups represented, etc.? Section 3
3 B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. See Section 3 and Appendix C
C 3 Did you run computational experiments? Section 4

heading: 3 C1. Did you report the number of parameters in the models used, the total computational budget
text: (e.g., GPU hours), and computing infrastructure used? See Appendix A
The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a question on AI writing assistance.

heading: 3 C2. Did you discuss the experimental setup, including hyperparameter search and best-found
text: hyperparameter values? See Appendix A

heading: 3 C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary
text: statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? Section 4 footnote 6

heading: 3 C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did
text: you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Section 4 footnote 5 and Appendix A
D 7 Did you use human annotators (e.g., crowdworkers) or research with human participants? Left blank.
D1. Did you report the full text of instructions given to participants, including e.g., screenshots, disclaimers of any risks to participants or annotators, etc.? No response.
D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants’ demographic (e.g., country of residence)? No response.
D3. Did you discuss whether and how consent was obtained from people whose data you’re using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? No response.
D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? No response.
D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data? No response.

heading: Future Work
text: 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 use-
ful 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 LRSum to further increase the quality and quantity of summarization data for less-resourced languages. Limitations
A limitation of this work is that the dataset has not yet been thoroughly vetted by native speakers of the languages contained in the dataset. We acknowledge the importance of working with native speakers and manually reviewing datasets in greater detail as argued for by Kreutzer et al. (2022) and Lignos et al. (2022). We hope to do more manual review of LR-Sum and other summarization datasets in the near future