metadata

license: cc-by-sa-4.0
task_categories:
  - summarization
  - text-retrieval
  - text-generation
  - text2text-generation
language:
  - af
  - ar
  - az
  - bn
  - cs
  - de
  - en
  - es
  - et
  - fa
  - fi
  - fr
  - ga
  - gl
  - gu
  - he
  - hi
  - hr
  - id
  - it
  - ja
  - ka
  - kk
  - km
  - ko
  - lt
  - lv
  - mk
  - ml
  - mn
  - mr
  - my
  - ne
  - nl
  - pl
  - ps
  - pt
  - ro
  - ru
  - si
  - sl
  - sv
  - ta
  - th
  - tr
  - uk
  - ur
  - vi
  - xh
  - zh
pretty_name: MegaWika-Report-Generation

Dataset Card for MegaWika for Report Generation

Dataset Description

Homepage: HuggingFace
Repository: HuggingFace
Paper: link
Point of Contact: Samuel Barham

Dataset Summary

MegaWika is a multi- and crosslingual text dataset containing 30 million Wikipedia passages with their scraped and cleaned web citations. The passages span 50 Wikipedias in 50 languages, and the articles in which the passages were originally embedded are included for convenience. Where a Wikipedia passage is in a non-English language, an automated English translation is provided.

This dataset provides the data for report generation / multi-document summarization with information retrieval.

Dataset Creation

See the original HuggingFace repo.

Languages

MegaWika is divided by Wikipedia language. There are 50 languages, including English, each designated by their 2-character ISO language code.

Languages

MegaWika is divided by Wikipedia language. There are 50 languages, including English, each designated by their 2-character ISO language code:

af: Afrikaans
ar: Arabic
az: Azeri (Azerbaijani)
bn: Bengali
cs: Czech
de: German (Deutsch)
en: English
es: Spanish (Español)
et: Estonian
fa: Farsi (Persian)
fi: Finnish
fr: French
ga: Irish (Gaelic)
gl: Galician
gu: Gujarati
he: Hebrew
hi: Hindi
hr: Hungarian
id: Indonesian
it: Italian
ja: Japanese
ka: Georgian (Kartvelian/Kartlian)
kk: Kazakh
km: Khmer
ko: Korean
lt: Lithuanian
lv: Latvian
mk: Macedonian (Makedonski)
ml: Malay (Malayalam)
mn: Mongolian
mr: Marathi
my: Burmese (Myanmar language)
ne: Nepali
nl: Dutch (Nederlands)
pl: Polish
ps: Pashto
pt: Portuguese
ro: Romanian
ru: Russian
si: Sinhalese (Sri Lankan language)
sl: Slovenian
sv: Swedish (Svenska)
ta: Tamil
th: Thai
tr: Turkish
uk: Ukrainian
ur: Urdu
vi: Vietnamese
xh: Xhosa
zh: Chinese (Zhōng wén)

Dataset Structure

The dataset is divided into two main sections (1) generating the entire Wikipedia sections from multiple citations ("all") or (2) generating segments of each section in an iterative fashion ("iterative"). Then the dataset is divided by language pairs. Note that each language can be used cross-lingually by using the en_gold_section_text key.

Data Instances

Given the rest of the fields (except for the ID) the goals is to produce the gold_section_text (e.g. given the title, intro, section name, and citations). num_docs is provided for filtering on the number of docs for the multi-doc summarization. Note that in the iterative setting is it just one citation. NOTE: num_docs is incorrect for now, will be updated.

Data Fields

The detailed structure of an instance is as follows:

{
  "id": <string : a unique id for the instance>
  "num_docs": <int : the number of citations for this instance>
  "title": <string : title of original Wikipedia article>
  "intro": <string : text of the Wikipedia article's introduction>
  "section_name": <string : the name of the section to generate>
  "previous_text": <string : used for the iterative task format, the previous text in the section already to condition on>
  "question": <string : a natural language question that could be used for query-focused summarization, generated by ChatGPT>
  "gold_section_text": <string : the text of the original Wikipedia section, e.g. the gold label for summarization>
  "en_gold_section_text": <string : the English version of the text from the original Wikipedia section, e.g. the gold label for cross-lingual summarization>
  "citations": <list of strings : the text of the citations (e.g. reference) for the section/chunk >
}

Licensing and Takedown

MegaWika 1.0 consists in part of documents scraped from across the web (based on citations linked in Wikipedia articles.)

We do not own any of the scraped text nor do we claim copyright: text drawn from Wikipedia citations are meant for research use in algorithmic design and model training.

We release this dataset and all its contents under CC-BY-SA-4.0.

Notice and Takedown Policy:

NB: Should you consider that our data contains material that is owned by you and should therefore not be reproduced here, please:

Clearly identify yourself, with detailed contact data such as an address, telephone number or email address at which you can be contacted.
Clearly identify the copyrighted work claimed to be infringed.
Clearly identify the material that is claimed to be infringing and information reasonably sufficient to allow us to locate the material.

And contact the authors.

Take down: We will comply to legitimate requests by removing the affected sources from the next release of the dataset.

Usage

# all of the dataset (not recommended)
dataset = load_dataset("hltcoe/megawika-report-generation")

# just the `all`` section data (all splits)
dataset = load_dataset("hltcoe/megawika-report-generation", data_dir="all")

# just the `all` English test set (can replace with "validation" or "train", or other langs)
dataset = load_dataset("hltcoe/megawika-report-generation", data_dir="all/en", split="test")

Dataset Curators

Released and maintained by the Johns Hopkins University Human Language Technology Center of Excellence (JHU/HLTCOE). You can contact one the MegaWika authors, including Samuel Barham, Orion Weller, and Ben van Durme with questions.

Licensing Information

Released under the Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) license.

Citation Information

@misc{barham2023megawika,
      title={MegaWika: Millions of reports and their sources across 50 diverse languages}, 
      author={Samuel Barham and and  Weller and Michelle Yuan and Kenton Murray and Mahsa Yarmohammadi and Zhengping Jiang and Siddharth Vashishtha and Alexander Martin and Anqi Liu and Aaron Steven White and Jordan Boyd-Graber and Benjamin Van Durme},
      year={2023},
      eprint={2307.07049},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}