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---
license: cc-by-4.0
task_categories:
- summarization
- text-generation
annotations_creators:
- found
language_creators:
- found
language:
- am
- az
- bn
- bo
- bs
- ku
- zh
- el
- en
- fa
- fr
- ht
- ha
- hy
- id
- ka
- km
- rw
- ko
- lo
- mk
- my
- nd
- pt
- ps
- ru
- sn
- so
- es
- sq
- sr
- sw
- th
- ti
- tr
- uk
- ur
- uz
- vi
pretty_name: LR-Sum
size_categories:
- 100K<n<1M
multilinguality:
- multilingual
tags:
- conditional-text-generation
viewer: true
configs:
- config_name: amh
data_files:
- split: test
path: amh/test-*
- config_name: aze
data_files:
- split: test
path: aze/test-*
- split: train
path: aze/train-*
- split: validation
path: aze/validation-*
- config_name: ben
data_files:
- split: test
path: ben/test-*
- config_name: bod
data_files:
- split: test
path: bod/test-*
- config_name: bos
data_files:
- split: test
path: bos/test-*
- split: train
path: bos/train-*
- split: validation
path: bos/validation-*
- config_name: ckb
data_files:
- split: test
path: ckb/test-*
- split: train
path: ckb/train-*
- split: validation
path: ckb/validation-*
- config_name: cmn_s
data_files:
- split: test
path: cmn_s/test-*
- split: train
path: cmn_s/train-*
- split: validation
path: cmn_s/validation-*
- config_name: cmn_t
data_files:
- split: test
path: cmn_t/test-*
- split: train
path: cmn_t/train-*
- split: validation
path: cmn_t/validation-*
- config_name: ell
data_files:
- split: test
path: ell/test-*
- config_name: eng
data_files:
- split: test
path: eng/test-*
- split: train
path: eng/train-*
- split: validation
path: eng/validation-*
- config_name: fas
data_files:
- split: test
path: fas/test-*
- split: train
path: fas/train-*
- split: validation
path: fas/validation-*
- config_name: fra
data_files:
- split: test
path: fra/test-*
- split: train
path: fra/train-*
- split: validation
path: fra/validation-*
- config_name: hat
data_files:
- split: test
path: hat/test-*
- split: train
path: hat/train-*
- split: validation
path: hat/validation-*
- config_name: hau
data_files:
- split: test
path: hau/test-*
- config_name: hye
data_files:
- split: test
path: hye/test-*
- split: train
path: hye/train-*
- split: validation
path: hye/validation-*
- config_name: ind
data_files:
- split: test
path: ind/test-*
- split: train
path: ind/train-*
- split: validation
path: ind/validation-*
- config_name: kat
data_files:
- split: test
path: kat/test-*
- split: train
path: kat/train-*
- split: validation
path: kat/validation-*
- config_name: khm
data_files:
- split: test
path: khm/test-*
- split: train
path: khm/train-*
- split: validation
path: khm/validation-*
- config_name: kin
data_files:
- split: test
path: kin/test-*
- config_name: kmr
data_files:
- split: test
path: kmr/test-*
- split: train
path: kmr/train-*
- split: validation
path: kmr/validation-*
- config_name: kor
data_files:
- split: test
path: kor/test-*
- split: train
path: kor/train-*
- split: validation
path: kor/validation-*
- config_name: lao
data_files:
- split: test
path: lao/test-*
- split: train
path: lao/train-*
- split: validation
path: lao/validation-*
- config_name: mkd
data_files:
- split: test
path: mkd/test-*
- split: train
path: mkd/train-*
- split: validation
path: mkd/validation-*
- config_name: mya
data_files:
- split: test
path: mya/test-*
- split: train
path: mya/train-*
- split: validation
path: mya/validation-*
- config_name: nde
data_files:
- split: test
path: nde/test-*
- split: train
path: nde/train-*
- split: validation
path: nde/validation-*
- config_name: por
data_files:
- split: test
path: por/test-*
- split: train
path: por/train-*
- split: validation
path: por/validation-*
- config_name: prs
data_files:
- split: test
path: prs/test-*
- split: train
path: prs/train-*
- split: validation
path: prs/validation-*
- config_name: pus
data_files:
- split: test
path: pus/test-*
- split: train
path: pus/train-*
- split: validation
path: pus/validation-*
- config_name: rus
data_files:
- split: test
path: rus/test-*
- split: train
path: rus/train-*
- split: validation
path: rus/validation-*
- config_name: sna
data_files:
- split: test
path: sna/test-*
- split: train
path: sna/train-*
- split: validation
path: sna/validation-*
- config_name: som
data_files:
- split: test
path: som/test-*
- config_name: spa
data_files:
- split: test
path: spa/test-*
- split: train
path: spa/train-*
- split: validation
path: spa/validation-*
- config_name: sqi
data_files:
- split: test
path: sqi/test-*
- split: train
path: sqi/train-*
- split: validation
path: sqi/validation-*
- config_name: srp
data_files:
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path: srp/test-*
- split: train
path: srp/train-*
- split: validation
path: srp/validation-*
- config_name: swh
data_files:
- split: test
path: swh/test-*
- config_name: tha
data_files:
- split: test
path: tha/test-*
- split: train
path: tha/train-*
- split: validation
path: tha/validation-*
- config_name: tir
data_files:
- split: test
path: tir/test-*
- config_name: tur
data_files:
- split: test
path: tur/test-*
- split: train
path: tur/train-*
- split: validation
path: tur/validation-*
- config_name: ukr
data_files:
- split: test
path: ukr/test-*
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path: ukr/train-*
- split: validation
path: ukr/validation-*
- config_name: urd
data_files:
- split: test
path: urd/test-*
- split: train
path: urd/train-*
- split: validation
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- config_name: uzb
data_files:
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- split: train
path: uzb/train-*
- split: validation
path: uzb/validation-*
- config_name: vie
data_files:
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path: vie/test-*
- split: train
path: vie/train-*
- split: validation
path: vie/validation-*
dataset_info:
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- config_name: ben
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---
# Dataset Card for LR-Sum
LR-Sum is a automatic summarization dataset of newswire text with a focus on less resourced languages with a cc-by 4.0 license.
## Dataset Details
### Dataset Description
LR-Sum is a 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 39 languages, many of which are less-resourced.
The data is based on the collection of the Multilingual Open Text corpus where the source data is public domain newswire collected from from Voice of America websites.
LR-Sum is released under a Creative Commons license (CC BY 4.0), making it one of the most openly-licensed multilingual summarization datasets.
- **Curated by:** BLT Lab: Chester Palen-Michel and Constantine Lignos
- **Shared by:** Chester Palen-Michel
- **Language(s) (NLP):** Albanian, Amharic, Armenian, Azerbaijani, Bengali, Bosnian, Burmese, Chinese, English, French, Georgian, Greek, Haitian Creole, Hausa, Indonesian, Khmer, Kinyarwanda, Korean, Kurdish, Lao, Macedonian, Northern Ndebele, Pashto, Persian, Portuguese, Russian, Serbian, Shona, Somali, Spanish, Swahili, Thai, Tibetan, Tigrinya, Turkish, Ukrainian, Urdu, Uzbek, Vietnamese
- **License:** CC-BY 4.0
### Dataset Sources [optional]
Multilingual Open Text v1.6
which is a collection of newswire text from Voice of America (VOA).
- **Paper:** [https://aclanthology.org/2023.findings-acl.427/](https://aclanthology.org/2023.findings-acl.427/)
- **Repository:** [https://github.com/bltlab/lr-sum](https://github.com/bltlab/lr-sum)
## Uses
The dataset is intended for research in automatic summarization in various languages, especially for less resourced languages.
### Direct Use
The data can be used for training text generation models to generate short summaries of news articles in many languages.
Automatic evaluation of automatic summarization is another use case, though we encourage also conducting human evaluation of any model trained for summarization.
### Out-of-Scope Use
This dataset only includes newswire text, so models trained on the data may not be effective for out of domain summarization.
## Dataset Structure
Each field is a string:
```
{
'id': Article unique id
'url': URL for the news article
'title': The title of the news article
'summary': The summary of the article
'text': The full text of the news article not including title
}
```
## Dataset Creation
### Curation Rationale
Research in automatic summarization for less resourced languages.
### Source Data
Voice of America (VOA)
#### Data Collection and Processing
See our [paper](https://aclanthology.org/2023.findings-acl.427/) for details on collection and processing.
#### Who are the source data producers?
Voice of America (VOA)
#### Annotation process
The summaries are found in news article meta data. More detail about the curation process can be found in our paper.
#### Who are the annotators?
The summaries are found in the news article meta data. The authors of the summaries are authors and staff for VOA.
#### Personal and Sensitive Information
The only sensative personal information would be information already published in news articles on VOA.
See [VOA's mission and values](https://www.insidevoa.com/p/5831.html#:~:text=VOA%20has%20a%20legal%20obligation,sites%20at%20the%20earliest%20opportunity.)
## Bias, Risks, and Limitations
The content in this dataset is newswire.
See [VOA's mission and values](https://www.insidevoa.com/p/5831.html#:~:text=VOA%20has%20a%20legal%20obligation,sites%20at%20the%20earliest%20opportunity.) for more detail about the journalistic integrity and policy.
### Recommendations
The data is newswire text. Training text generation models on this dataset will have similar risks and limitations to other text generation models including hallucinations and potentially inaccurate statements.
For some languages that have fewer examples, issues with text generation models are likely to be more pronounced.
The dataset is primarily released for research despite having a permissive license.
We encourage users to thoroughly test and evaluate any models trained using this data before putting them into production environments.
## Citation
If you make use of this dataset, please cite our paper using this bibtex:
**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",
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.",
}
```
## Dataset Card Authors
Chester Palen-Michel [@cpalenmichel](https://github.com/cpalenmichel)
## Dataset Card Contact
Chester Palen-Michel [@cpalenmichel](https://github.com/cpalenmichel) |