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Dataset Card for GEM/xsum
Link to Main Data Card
You can find the main data card on the GEM Website.
Dataset Summary
XSum is an English news summarization dataset where the task is to predict the first sentence of an article from the rest of it.
You can load the dataset via:
import datasets
data = datasets.load_dataset('GEM/xsum')
The data loader can be found here.
website
n/a
paper
authors
Shashi Narayan, Shay B. Cohen, Mirella Lapata (all affiliated with University of Edinburgh at the time of dataset creation)
Dataset Overview
Where to find the Data and its Documentation
Download
Paper
BibTex
@InProceedings{xsum-emnlp,
author = "Shashi Narayan and Shay B. Cohen and Mirella Lapata",
title = "Don't Give Me the Details, Just the Summary! {T}opic-Aware Convolutional Neural Networks for Extreme Summarization",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing ",
year = "2018",
address = "Brussels, Belgium",
}
Contact Name
Shashi Narayan
Contact Email
Has a Leaderboard?
no
Languages and Intended Use
Multilingual?
no
Covered Dialects
Since the source of the dataset are BBC articles, the language is in British English of the variation written by journalists.
Covered Languages
English
Whose Language?
Professional journalists
License
cc-by-sa-4.0: Creative Commons Attribution Share Alike 4.0 International
Intended Use
The dataset is for the task of abstractive summarization in its extreme form, its about summarizing a document in a single sentence. The idea is to create a short, one-sentence news summary answering the question "What is the article about?".
Primary Task
Summarization
Communicative Goal
Given a news article, produce a single sentence summary of the content of the article.
Credit
Curation Organization Type(s)
academic
Curation Organization(s)
University of Edinburgh
Dataset Creators
Shashi Narayan, Shay B. Cohen, Mirella Lapata (all affiliated with University of Edinburgh at the time of dataset creation)
Funding
European Research Council (Lapata; award number 681760), the European Union under the Horizon 2020 SUMMA project (Narayan, Cohen; grant agreement 688139), and Huawei Technologies (Cohen).
Who added the Dataset to GEM?
The original data card was written by Laura Perez-Beltrachini and the data loader by Yacine Jernite. Sebastian Gehrmann migrated the data card to the new format and extended it. The v2 data loader was migrated by Abinaya Mahendiran
Dataset Structure
Data Fields
Document: Input news article.Summary: One sentence summary of the article.Id: BBC ID of the article.
Reason for Structure
The Document/Summary format is standard for summarization datasets.
How were labels chosen?
The labels are the first sentence of the source article.
Example Instance
{
'document': 'The researchers have sequenced the genome of a strain of bacterium that causes the virulent infection.\nA survey in 2007 showed that bleeding canker had spread rapidly, with almost half of the two million horse chestnuts displaying symptoms of the disease.\nThe findings have been published in the journal PLoS One.\nA visible symptom of the disease is a lesion on the bark, which oozes a resin on to the trunk or sometimes the branches.\nThe bark underneath the canker is killed, and if cankers manage to go all the way around the trunk then the horse chestnut (Aesculus hippocastanum) will die because it cuts off the food supply. [...]',
'target': "A team of UK scientists hopes to shed light on the mysteries of bleeding canker, a disease that is threatening the nation's horse chestnut trees.",
}
Data Splits
| Section | Number of Documents |
|---|---|
| Training | 204,045 |
| Validation | 11,332 |
| Testing | 11,334 |
| Total | 226k |
| Section | number of words | number of sentences |
|---|---|---|
| Documents | 431.07 | 19.77 |
| Summary | 23.26 | 1.00 |
Splitting Criteria
The identifiers in the URLs were used to randomly split the dataset into training (90%, 204,045), validation (5%, 11,332), and test (5%, 11,334) sets.
Dataset Curation
Original Curation
Original Curation Rationale
Comparable datasets are often very extractive which is not a strategy that works for one-sentence summaries. The dataset curators thus created this dataset as a way to evaluate truly abstractive models
Communicative Goal
Same as the communicative goal in GEM: A model should summarize a news article in a single sentence
Sourced from Different Sources
no
Language Data
How was Language Data Obtained?
Found
Where was it found?
Single website
Language Producers
The data was collected from articles between 2010 and 2017. No other information
Topics Covered
The collected articles included the following topics: News, Politics, Sports, Weather, Business, Technology, Science, Health, Family, Education, Entertainment and Arts
The dataset curators also used LDA to gain insight into this question and found that the following were the top keywords associated with each topic:
- T1: charge, court, murder, police, arrest, guilty, sentence, boy, bail, space, crown, trial
- T2: church, abuse, bishop, child, catholic, gay, pope, school, christian, priest, cardinal
- T3: council, people, government, local, housing, home, house, property, city, plan, authority
- T4: clinton, party, trump, climate, poll, vote, plaid, election, debate, change, candidate, campaign
- T5: country, growth, report, business, export, fall, bank, security, economy, rise, global, inflation
- T6: hospital, patient, trust, nhs, people, care, health, service, staff, report, review, system, child
Data Validation
not validated
Data Preprocessing
The text was extracted from the HTML of the webpage. No further processing was done.
Was Data Filtered?
not filtered
Structured Annotations
Additional Annotations?
none
Annotation Service?
no
Consent
Any Consent Policy?
no
Justification for Using the Data
The copyright license of the data allows reusing it for this purpose.
Private Identifying Information (PII)
Contains PII?
yes/very 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?
no
Discussion of Biases
Any Documented Social Biases?
unsure
Are the Language Producers Representative of the Language?
The language and content of the data is focused on news and language in the UK and as such not representative of the speakers world-wide. Existing selection biases of the BBC exist in this dataset.
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