lhoorie/lovelyBERT
Text Classification
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Dataset Card for GEM/xwikis
Link to Main Data Card
You can find the main data card on the GEM Website.
Dataset Summary
The XWikis Corpus provides datasets with different language pairs and directions for cross-lingual and multi-lingual abstractive document summarisation.
You can load the dataset via:
import datasets
data = datasets.load_dataset('GEM/xwikis')
The data loader can be found here.
website
paper
https://arxiv.org/abs/2202.09583
authors
Laura Perez-Beltrachini (University of Edinburgh)
Dataset Overview
Where to find the Data and its Documentation
Webpage
Paper
https://arxiv.org/abs/2202.09583
BibTex
@InProceedings{clads-emnlp,
author = "Laura Perez-Beltrachini and Mirella Lapata",
title = "Models and Datasets for Cross-Lingual Summarisation",
booktitle = "Proceedings of The 2021 Conference on Empirical Methods in Natural Language Processing ",
year = "2021",
address = "Punta Cana, Dominican Republic",
}
Contact Name
Laura Perez-Beltrachini
Contact Email
Has a Leaderboard?
no
Languages and Intended Use
Multilingual?
yes
Covered Languages
German, English, French, Czech, Chinese
License
cc-by-sa-4.0: Creative Commons Attribution Share Alike 4.0 International
Intended Use
Cross-lingual and Multi-lingual single long input document abstractive summarisation.
Primary Task
Summarization
Communicative Goal
Entity descriptive summarisation, that is, generate a summary that conveys the most salient facts of a document related to a given entity.
Credit
Curation Organization Type(s)
academic
Dataset Creators
Laura Perez-Beltrachini (University of Edinburgh)
Who added the Dataset to GEM?
Laura Perez-Beltrachini (University of Edinburgh) and Ronald Cardenas (University of Edinburgh)
Dataset Structure
Data Splits
For each language pair and direction there exists a train/valid/test split. The test split is a sample of size 7k from the intersection of titles existing in the four languages (cs,fr,en,de). Train/valid are randomly split.
Dataset in GEM
Rationale for Inclusion in GEM
Similar Datasets
no
GEM-Specific Curation
Modificatied for GEM?
no
Additional Splits?
no
Getting Started with the Task
Previous Results
Previous Results
Measured Model Abilities
- identification of entity salient information
- translation
- multi-linguality
- cross-lingual transfer, zero-shot, few-shot
Metrics
ROUGE
Previous results available?
yes
Other Evaluation Approaches
ROUGE-1/2/L
Dataset Curation
Original Curation
Sourced from Different Sources
no
Language Data
How was Language Data Obtained?
Found
Where was it found?
Single website
Data Validation
other
Was Data Filtered?
not filtered
Structured Annotations
Additional Annotations?
found
Annotation Service?
no
Annotation Values
The input documents have section structure information.
Any Quality Control?
validated by another rater
Quality Control Details
Bilingual annotators assessed the content overlap of source document and target summaries.
Consent
Any Consent Policy?
no
Private Identifying Information (PII)
Contains PII?
no PII
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?
no
Considerations for Using the Data
PII Risks and Liability
Licenses
Copyright Restrictions on the Dataset
public domain
Copyright Restrictions on the Language Data
public domain
Known Technical Limitations
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