metadata

task_categories:
  - conditional-text-generation
task_ids:
  - summarization
language:
  - am
  - ar
  - az
  - bn
  - my
  - zh
  - en
  - fr
  - gu
  - ha
  - hi
  - ig
  - id
  - ja
  - rn
  - ko
  - ky
  - mr
  - ne
  - om
  - ps
  - fa
  - pcm
  - pt
  - pa
  - ru
  - gd
  - sr
  - si
  - so
  - es
  - sw
  - ta
  - te
  - th
  - ti
  - tr
  - uk
  - ur
  - uz
  - vi
  - cy
  - yo
size_categories:
  - 1M<n<10M
license:
  - cc-by-nc-sa-4.0
multilinguality:
  - multilingual
source_datasets:
  - original
paperswithcode_id: xl-sum
annotations_creators:
  - found
language_creators:
  - found
pretty_name: XL-Sum

Dataset Card for "XL-Sum"

Dataset Card Creation Guide

Dataset Description

Repository: https://github.com/csebuetnlp/xl-sum
Paper: XL-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages
Point of Contact: Tahmid Hasan

Dataset Summary

We present XLSum, a comprehensive and diverse dataset comprising 1.35 million professionally annotated article-summary pairs from BBC, extracted using a set of carefully designed heuristics. The dataset covers 45 languages ranging from low to high-resource, for many of which no public dataset is currently available. XL-Sum is highly abstractive, concise, and of high quality, as indicated by human and intrinsic evaluation.

Supported Tasks and Leaderboards

More information needed

Languages

amharic
arabic
azerbaijani
bengali
burmese
chinese_simplified
chinese_traditional
english
french
gujarati
hausa
hindi
igbo
indonesian
japanese
kirundi
korean
kyrgyz
marathi
nepali
oromo
pashto
persian
pidgin
portuguese
punjabi
russian
scottish_gaelic
serbian_cyrillic
serbian_latin
sinhala
somali
spanish
swahili
tamil
telugu
thai
tigrinya
turkish
ukrainian
urdu
uzbek
vietnamese
welsh
yoruba

Dataset Structure

Data Instances

One example from the English dataset is given below in JSON format.

{
  "id": "technology-17657859",
  "url": "https://www.bbc.com/news/technology-17657859",
  "title": "Yahoo files e-book advert system patent applications",
  "summary": "Yahoo has signalled it is investigating e-book adverts as a way to stimulate its earnings.",
  "text": "Yahoo's patents suggest users could weigh the type of ads against the sizes of discount before purchase. It says in two US patent applications that ads for digital book readers have been \"less than optimal\" to date. The filings suggest that users could be offered titles at a variety of prices depending on the ads' prominence They add that the products shown could be determined by the type of book being read, or even the contents of a specific chapter, phrase or word. The paperwork was published by the US Patent and Trademark Office late last week and relates to work carried out at the firm's headquarters in Sunnyvale, California. \"Greater levels of advertising, which may be more valuable to an advertiser and potentially more distracting to an e-book reader, may warrant higher discounts,\" it states. Free books It suggests users could be offered ads as hyperlinks based within the book's text, in-laid text or even \"dynamic content\" such as video. Another idea suggests boxes at the bottom of a page could trail later chapters or quotes saying \"brought to you by Company A\". It adds that the more willing the customer is to see the ads, the greater the potential discount. \"Higher frequencies... may even be great enough to allow the e-book to be obtained for free,\" it states. The authors write that the type of ad could influence the value of the discount, with \"lower class advertising... such as teeth whitener advertisements\" offering a cheaper price than \"high\" or \"middle class\" adverts, for things like pizza. The inventors also suggest that ads could be linked to the mood or emotional state the reader is in as a they progress through a title. For example, they say if characters fall in love or show affection during a chapter, then ads for flowers or entertainment could be triggered. The patents also suggest this could applied to children's books - giving the Tom Hanks animated film Polar Express as an example. It says a scene showing a waiter giving the protagonists hot drinks \"may be an excellent opportunity to show an advertisement for hot cocoa, or a branded chocolate bar\". Another example states: \"If the setting includes young characters, a Coke advertisement could be provided, inviting the reader to enjoy a glass of Coke with his book, and providing a graphic of a cool glass.\" It adds that such targeting could be further enhanced by taking account of previous titles the owner has bought. 'Advertising-free zone' At present, several Amazon and Kobo e-book readers offer full-screen adverts when the device is switched off and show smaller ads on their menu screens, but the main text of the titles remains free of marketing. Yahoo does not currently provide ads to these devices, and a move into the area could boost its shrinking revenues. However, Philip Jones, deputy editor of the Bookseller magazine, said that the internet firm might struggle to get some of its ideas adopted. \"This has been mooted before and was fairly well decried,\" he said. \"Perhaps in a limited context it could work if the merchandise was strongly related to the title and was kept away from the text. \"But readers - particularly parents - like the fact that reading is an advertising-free zone. Authors would also want something to say about ads interrupting their narrative flow.\""
}

Data Fields

'id': A string representing the article ID.
'url': A string representing the article URL.
'title': A string containing the article title.
'summary': A string containing the article summary.
'text' : A string containing the article text.

Data Splits

We used a 80%-10%-10% split for all languages with a few exceptions. English was split 93%-3.5%-3.5% for the evaluation set size to resemble that of CNN/DM and XSum; Scottish Gaelic, Kyrgyz and Sinhala had relatively fewer samples, their evaluation sets were increased to 500 samples for more reliable evaluation. Same articles were used for evaluation in the two variants of Chinese and Serbian to prevent data leakage in multilingual training. Individual dataset download links with train-dev-test example counts are given below:

Language	ISO 639-1 Code	BBC subdomain(s)	Train	Dev	Test	Total
Amharic	am	https://www.bbc.com/amharic	5761	719	719	7199
Arabic	ar	https://www.bbc.com/arabic	37519	4689	4689	46897
Azerbaijani	az	https://www.bbc.com/azeri	6478	809	809	8096
Bengali	bn	https://www.bbc.com/bengali	8102	1012	1012	10126
Burmese	my	https://www.bbc.com/burmese	4569	570	570	5709
Chinese (Simplified)	zh-CN	https://www.bbc.com/ukchina/simp, https://www.bbc.com/zhongwen/simp	37362	4670	4670	46702
Chinese (Traditional)	zh-TW	https://www.bbc.com/ukchina/trad, https://www.bbc.com/zhongwen/trad	37373	4670	4670	46713
English	en	https://www.bbc.com/english, https://www.bbc.com/sinhala `*`	306522	11535	11535	329592
French	fr	https://www.bbc.com/afrique	8697	1086	1086	10869
Gujarati	gu	https://www.bbc.com/gujarati	9119	1139	1139	11397
Hausa	ha	https://www.bbc.com/hausa	6418	802	802	8022
Hindi	hi	https://www.bbc.com/hindi	70778	8847	8847	88472
Igbo	ig	https://www.bbc.com/igbo	4183	522	522	5227
Indonesian	id	https://www.bbc.com/indonesia	38242	4780	4780	47802
Japanese	ja	https://www.bbc.com/japanese	7113	889	889	8891
Kirundi	rn	https://www.bbc.com/gahuza	5746	718	718	7182
Korean	ko	https://www.bbc.com/korean	4407	550	550	5507
Kyrgyz	ky	https://www.bbc.com/kyrgyz	2266	500	500	3266
Marathi	mr	https://www.bbc.com/marathi	10903	1362	1362	13627
Nepali	np	https://www.bbc.com/nepali	5808	725	725	7258
Oromo	om	https://www.bbc.com/afaanoromoo	6063	757	757	7577
Pashto	ps	https://www.bbc.com/pashto	14353	1794	1794	17941
Persian	fa	https://www.bbc.com/persian	47251	5906	5906	59063
Pidgin`**`	n/a	https://www.bbc.com/pidgin	9208	1151	1151	11510
Portuguese	pt	https://www.bbc.com/portuguese	57402	7175	7175	71752
Punjabi	pa	https://www.bbc.com/punjabi	8215	1026	1026	10267
Russian	ru	https://www.bbc.com/russian, https://www.bbc.com/ukrainian `*`	62243	7780	7780	77803
Scottish Gaelic	gd	https://www.bbc.com/naidheachdan	1313	500	500	2313
Serbian (Cyrillic)	sr	https://www.bbc.com/serbian/cyr	7275	909	909	9093
Serbian (Latin)	sr	https://www.bbc.com/serbian/lat	7276	909	909	9094
Sinhala	si	https://www.bbc.com/sinhala	3249	500	500	4249
Somali	so	https://www.bbc.com/somali	5962	745	745	7452
Spanish	es	https://www.bbc.com/mundo	38110	4763	4763	47636
Swahili	sw	https://www.bbc.com/swahili	7898	987	987	9872
Tamil	ta	https://www.bbc.com/tamil	16222	2027	2027	20276
Telugu	te	https://www.bbc.com/telugu	10421	1302	1302	13025
Thai	th	https://www.bbc.com/thai	6616	826	826	8268
Tigrinya	ti	https://www.bbc.com/tigrinya	5451	681	681	6813
Turkish	tr	https://www.bbc.com/turkce	27176	3397	3397	33970
Ukrainian	uk	https://www.bbc.com/ukrainian	43201	5399	5399	53999
Urdu	ur	https://www.bbc.com/urdu	67665	8458	8458	84581
Uzbek	uz	https://www.bbc.com/uzbek	4728	590	590	5908
Vietnamese	vi	https://www.bbc.com/vietnamese	32111	4013	4013	40137
Welsh	cy	https://www.bbc.com/cymrufyw	9732	1216	1216	12164
Yoruba	yo	https://www.bbc.com/yoruba	6350	793	793	7936

* A lot of articles in BBC Sinhala and BBC Ukrainian were written in English and Russian respectively. They were identified using Fasttext and moved accordingly.

** West African Pidgin English

Dataset Creation

Considerations for Using the Data

Social Impact of Dataset

More information needed

Discussion of Biases

More information needed

Other Known Limitations

More information needed

Additional Information

Dataset Curators

More information needed

Licensing Information

Contents of this repository are restricted to only non-commercial research purposes under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0). Copyright of the dataset contents belongs to the original copyright holders.

Citation Information

If you use any of the datasets, models or code modules, please cite the following paper:

@inproceedings{hasan-etal-2021-xl,
    title = "{XL}-Sum: Large-Scale Multilingual Abstractive Summarization for 44 Languages",
    author = "Hasan, Tahmid  and
      Bhattacharjee, Abhik  and
      Islam, Md. Saiful  and
      Mubasshir, Kazi  and
      Li, Yuan-Fang  and
      Kang, Yong-Bin  and
      Rahman, M. Sohel  and
      Shahriyar, Rifat",
    booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.findings-acl.413",
    pages = "4693--4703",
}

Contributions

Thanks to @abhik1505040 and @Tahmid for adding this dataset.

Datasets:

csebuetnlp
/

xlsum

Dataset Card for "XL-Sum"

Table of Contents

Dataset Description

Dataset Summary

Supported Tasks and Leaderboards

Languages

Dataset Structure

Data Instances

Data Fields

Data Splits

Dataset Creation

Curation Rationale

Source Data

Initial Data Collection and Normalization

Who are the source language producers?

Annotations

Annotation process

Who are the annotators?

Personal and Sensitive Information

Considerations for Using the Data

Social Impact of Dataset

Discussion of Biases

Other Known Limitations

Additional Information

Dataset Curators

Licensing Information

Citation Information

Contributions