# LR-Sum: Summarization for Less-Resourced Languages

arXiv: https://arxiv.org/abs/2212.09674 · HTML: https://mlsanity.com/paper/2212.09674/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 42 references that are in the map.
- First posted on arXiv 19 Dec 2022.

## Builds on (most cited first, 19 of 19)

- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization](https://mlsanity.com/paper/1808.08745/) (2018)
- [Teaching Machines to Read and Comprehend](https://mlsanity.com/paper/1506.03340/) (2015)
- [Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond](https://mlsanity.com/paper/1602.06023/) (2016)
- [XL-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages](https://mlsanity.com/paper/2106.13822/) (2021)
- [SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documents](https://mlsanity.com/paper/1611.04230/) (2016)
- [Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets](https://mlsanity.com/paper/2103.12028/) (2021)
- [Ranking Sentences for Extractive Summarization with Reinforcement Learning](https://mlsanity.com/paper/1802.08636/) (2018)
- [MLSUM: The Multilingual Summarization Corpus](https://mlsanity.com/paper/2004.14900/) (2020)
- [KINNEWS and KIRNEWS: Benchmarking Cross-Lingual Text Classification for Kinyarwanda and Kirundi](https://mlsanity.com/paper/2010.12174/) (2020)
- [Not always about you: Prioritizing community needs when developing endangered language technology](https://mlsanity.com/paper/2204.05541/) (2022)
- [Neural Latent Extractive Document Summarization](https://mlsanity.com/paper/1808.07187/) (2018)
- [Investigating an approach for low resource language dataset creation, curation and classification: Setswana and Sepedi](https://mlsanity.com/paper/2003.04986/) (2020)
- [Liputan6: A Large-scale Indonesian Dataset for Text Summarization](https://mlsanity.com/paper/2011.00679/) (2020)
- [Toward More Meaningful Resources for Lower-resourced Languages](https://mlsanity.com/paper/2202.12288/) (2022)
- [An Amharic News Text classification Dataset](https://mlsanity.com/paper/2103.05639/) (2021)
- [Multilingual Open Text Release 1: Public Domain News in 44 Languages](https://mlsanity.com/paper/2201.05609/) (2022)
- [ARPA: Armenian Paraphrase Detection Corpus and Models](https://mlsanity.com/paper/2009.12615/) (2020)
- [Khmer Text Classification Using Word Embedding and Neural Networks](https://mlsanity.com/paper/2112.06748/) (2021)

## Built on by (most cited first, 5 of 5)

- [SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages](https://mlsanity.com/paper/2406.10118/) (2024)
- [The State and Fate of Summarization Datasets: A Survey](https://mlsanity.com/paper/2411.04585/) (2024)
- [Beyond One-Size-Fits-All Summarization: Customizing Summaries for Diverse Users](https://mlsanity.com/paper/2503.10675/) (2025)
- [PrahokBART: A Pre-trained Sequence-to-Sequence Model for Khmer Natural Language Generation](https://mlsanity.com/paper/2512.13552/) (2025)
- [Comparing Approaches to Automatic Summarization in Less-Resourced Languages](https://mlsanity.com/paper/2512.24410/) (2025)

## Abstract (arXiv)

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.