README.md

metadata

annotations_creators:
  - none
language_creators:
  - unknown
language:
  - und
license:
  - cc-by-nc-sa-4.0
multilinguality:
  - unknown
size_categories:
  - unknown
source_datasets:
  - original
task_categories:
  - summarization
task_ids: []
pretty_name: xlsum

Dataset Card for GEM/xlsum

Dataset Description

• Homepage: https://github.com/csebuetnlp/xl-sum
• Repository: https://huggingface.co/datasets/csebuetnlp/xlsum/tree/main/data
• Paper: https://aclanthology.org/2021.findings-acl.413/
• Leaderboard: http://explainaboard.nlpedia.ai/leaderboard/task_xlsum/
• Point of Contact: Tahmid Hasan

Link to Main Data Card

You can find the main data card on the GEM Website.

Dataset Summary

XLSum is a highly multilingual summarization dataset supporting 44 language. The data stems from BBC news articles.

You can load the dataset via:

import datasets
data = datasets.load_dataset('GEM/xlsum')

The data loader can be found here.

website

Github

paper

ACL Anthology

Dataset Overview

Where to find the Data and its Documentation

Webpage

Github

Download

Huggingface

Paper

ACL Anthology

BibTex

@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",
}

Contact Name

Tahmid Hasan

Contact Email

tahmidhasan@cse.buet.ac.bd

Has a Leaderboard?

yes

Leaderboard Link

Explainaboard

Leaderboard Details

The leaderboard ranks models based on ROUGE scores (R1/R2/RL) of the generated summaries.

Languages and Intended Use

Multilingual?

yes

Covered Languages

Amharic, Arabic, Azerbaijani, Bengali, Bangla, Burmese, Chinese (family), English, French, Gujarati, Hausa, Hindi, Igbo, Indonesian, Japanese, Rundi, Korean, Kirghiz, Kyrgyz, Marathi, Nepali (individual language), Oromo, Pushto, Pashto, Persian, Ghanaian Pidgin English, Portuguese, Panjabi, Punjabi, Russian, Scottish Gaelic, Gaelic, Serbian, Romano-Serbian, Sinhala, Sinhalese, Somali, Spanish, Castilian, Swahili (individual language), Kiswahili, Tamil, Telugu, Thai, Tigrinya, Turkish, Ukrainian, Urdu, Uzbek, Vietnamese, Welsh, Yoruba

License

cc-by-nc-sa-4.0: Creative Commons Attribution Non Commercial Share Alike 4.0 International

Intended Use

Abstractive summarization has centered around the English language, as most large abstractive summarization datasets are available in English only. Though there have been some recent efforts for curating multilingual abstractive summarization datasets, they are limited in terms of the number of languages covered, the number of training samples, or both. To this end, XL-Sum presents a large-scale abstractive summarization dataset of 1.35 million news articles from 45 languages crawled from the British Broadcasting Corporation website. It is intended to be used for both multilingual and per-language summarization tasks.

Primary Task

Summarization

Communicative Goal

Summarize news-like text in one of 45 languages.

Credit

Curation Organization Type(s)

academic

Curation Organization(s)

Bangladesh University of Engineering and Technology

Who added the Dataset to GEM?

Tahmid Hasan (Bangladesh University of Engineering and Technology), Abhik Bhattacharjee (Bangladesh University of Engineering and Technology)

Dataset Structure

Data Fields

• gem_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.

Example Instance

{
    "gem_id": "GEM-xlsum_english-train-1589",
    "url": "[BBC news](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 Splits

The splits in the dataset are specified by the language names, which are as follows:

• 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

Splitting Criteria

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

Rationale for Inclusion in GEM

Why is the Dataset in GEM?

Traditional abstractive text summarization has been centered around English and other high-resource languages. XL-Sum provides a large collection of high-quality article-summary pairs for 45 languages where the languages range from high-resource to extremely low-resource. This enables the research community to explore the summarization capabilities of different models for multiple languages and languages in isolation. We believe the addition of XL-Sum to GEM makes the domain of abstractive text summarization more diversified and inclusive to the research community. We hope our efforts in this work will encourage the community to push the boundaries of abstractive text summarization beyond the English language, especially for low and mid-resource languages, bringing technological advances to communities of these languages that have been traditionally under-served.

Similar Datasets

yes

Unique Language Coverage

yes

Difference from other GEM datasets

The summaries are highly concise and abstractive.

Ability that the Dataset measures

Conciseness, abstractiveness, and overall summarization capability.

GEM-Specific Curation

Modificatied for GEM?

no

Additional Splits?

no

Getting Started with the Task

Previous Results

Previous Results

Measured Model Abilities

Conciseness, abstractiveness, and overall summarization capability.

Metrics

ROUGE

Proposed Evaluation

ROUGE is the de facto evaluation metric used for text summarization. However, it was designed specifically for evaluating English texts. Due to the nature of the metric, scores are heavily dependent on text tokenization / stemming / unnecessary character removal, etc. Some modifications to the original ROUGE evaluation were done such as punctuation only removal, language specific tokenization/stemming to enable reliable comparison of source and target summaries across different scripts.

Previous results available?

no

Dataset Curation

Original Curation

Original Curation Rationale

State-of-the-art text summarization models are heavily data-driven, i.e., a large number of article-summary pairs are required to train them effectively. As a result, abstractive summarization has centered around the English language, as most large abstractive summarization datasets are available in English only. Though there have been some recent efforts for curating multilingual abstractive summarization datasets, they are limited in terms of the number of languages covered, the number of training samples, or both. To this end, we curate XL-Sum, a large-scale abstractive summarization dataset of 1.35 million news articles from 45 languages crawled from the British Broadcasting Corporation website.

Communicative Goal

Introduce new languages in the english-centric domain of abstractive text summarization and enable both multilingual and per-language summarization.

Sourced from Different Sources

yes

Source Details

British Broadcasting Corporation (BBC) news websites.

Language Data

How was Language Data Obtained?

Found

Where was it found?

Multiple websites

Language Producers

The language content was written by professional news editors hired by BBC.

Topics Covered

News

Data Validation

not validated

Data Preprocessing

We used 'NFKC' normalization on all text instances.

Was Data Filtered?

algorithmically

Filter Criteria

We designed a crawler to recursively crawl pages starting from the homepage by visiting different article links present in each page visited. We were able to take advantage of the fact that all BBC sites have somewhat similar structures, and were able to scrape articles from all sites. We discarded pages with no textual contents (mostly pages consisting of multimedia contents) before further processing. We designed a number of heuristics to make the extraction effective by carefully examining the HTML structures of the crawled pages:

1. The desired summary must be present within the beginning two paragraphs of an article.
2. The summary paragraph must have some portion of texts in bold format.
3. The summary paragraph may contain some hyperlinks that may not be bold. The proportion of bold texts and hyperlinked texts to the total length of the paragraph in consideration must be at least 95%.
4. All texts except the summary and the headline must be included in the input text (including image captions).
5. The input text must be at least twice as large as the summary.

Structured Annotations

Additional Annotations?

none

Annotation Service?

no

Consent

Any Consent Policy?

yes

Consent Policy Details

BBC's policy specifies that the text content within its websites can be used for non-commercial research only.

Private Identifying Information (PII)

Contains PII?

likely

Categories of PII

generic PII

Any PII Identification?

no identification

Maintenance

Any Maintenance Plan?

no

Broader Social Context

Previous Work on the Social Impact of the Dataset

Usage of Models based on the Data

no

Impact on Under-Served Communities

Addresses needs of underserved Communities?

yes

Details on how Dataset Addresses the Needs

This dataset introduces summarization corpus for many languages where there weren't any datasets like this curated before.

Discussion of Biases

Any Documented Social Biases?

no

Are the Language Producers Representative of the Language?

Yes

Considerations for Using the Data

PII Risks and Liability

Licenses

Copyright Restrictions on the Dataset

research use only, non-commercial use only

Copyright Restrictions on the Language Data

research use only, non-commercial use only

Known Technical Limitations

Technical Limitations

Human evaluation showed most languages had a high percentage of good summaries in the upper nineties, almost none of the summaries contained any conflicting information, while about one-third on average had information that was not directly inferrable from the source article. Since generally multiple articles are written regarding an important event, there could be an overlap between the training and evaluation data in terms on content.

Unsuited Applications

The dataset is limited to news domain only. Hence it wouldn't be advisable to use a model trained on this dataset for summarizing texts from a different domain i.e. literature, scientific text etc. Another pitfall could be hallucinations in the model generated summary.

Discouraged Use Cases

ROUGE evaluates the quality of the summary as a whole by considering up to 4-gram overlaps. Therefore, in an article about India if the word "India" in the generated summary gets replaced by "Pakistan" due to model hallucination, the overall score wouldn't be reduced significantly, but the entire meaning could get changed.