metadata

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: false

Dataset Card for LR-Sum

This dataset card aims to be a base template for new datasets. It has been generated using this raw template.

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 40 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).

Repository: https://github.com/bltlab/lr-sum
Paper: https://aclanthology.org/2023.findings-acl.427/

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

[More Information Needed]

Dataset Creation

Curation Rationale

[More Information Needed]

Source Data

Data Collection and Processing

[More Information Needed]

Who are the source data producers?

[More Information Needed]

Annotations [optional]

Annotation process

[More Information Needed]

Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

[More Information Needed]

Bias, Risks, and Limitations

[More Information Needed]

Recommendations

Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.

Citation

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.",
}

APA:

Palen-Michel, C. & Lignos, C. (2023). LR-Sum: Summarization for Less-Resourced Languages. In Findings of the Association for Computational Linguistics: ACL 2023, pages 6829–6844, Toronto, Canada. Association for Computational Linguistics.

Dataset Card Authors [optional]

Chester Palen-Michel

Dataset Card Contact

Chester Palen-Michel