Datasets:
annotations_creators:
- automatically-created
language_creators:
- unknown
language:
- en
license:
- cc-by-sa-3.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- summarization
task_ids: []
pretty_name: wiki_cat_sum
Dataset Card for GEM/wiki_cat_sum
Dataset Description
- Homepage: https://github.com/lauhaide/WikiCatSum
- Repository: https://datashare.ed.ac.uk/handle/10283/3368
- Paper: https://arxiv.org/abs/1906.04687
- Leaderboard: N/A
- Point of Contact: Laura Perez-Beltrachini
Link to Main Data Card
You can find the main data card on the GEM Website.
Dataset Summary
WikiCatSum is an English summarization dataset in three domains: animals, companies, and film. It provides multiple paragraphs of text paired with a summary of the paragraphs.
You can load the dataset via:
import datasets
data = datasets.load_dataset('GEM/wiki_cat_sum')
The data loader can be found here.
website
paper
authors
Laura Perez-Beltrachini, Yang Liu, Mirella Lapata (University of Edinburgh) Peter J. Liu, Mohammad Saleh, Etienne Pot, Ben Goodrich, Ryan Sepassi, Lukasz Kaiser, Noam Shazeer (GoogleBrain)
Dataset Overview
Where to find the Data and its Documentation
Webpage
Download
Paper
BibTex
@inproceedings{perez-beltrachini-etal-2019-generating,
title = "Generating Summaries with Topic Templates and Structured Convolutional Decoders",
author = "Perez-Beltrachini, Laura and
Liu, Yang and
Lapata, Mirella",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1504",
doi = "10.18653/v1/P19-1504",
}
Contact Name
Laura Perez-Beltrachini
Contact Email
Has a Leaderboard?
no
Languages and Intended Use
Multilingual?
no
Covered Languages
English
License
cc-by-sa-3.0: Creative Commons Attribution Share Alike 3.0 Unported
Intended Use
Research on multi-document abstractive summarisation.
Primary Task
Summarization
Communicative Goal
Summarise the most important facts of a given entity in the Film, Company, and Animal domains from a cluster of related documents.
Credit
Curation Organization Type(s)
industry, academic
Curation Organization(s)
Google Cloud Platform, University of Edinburgh
Dataset Creators
Laura Perez-Beltrachini, Yang Liu, Mirella Lapata (University of Edinburgh) Peter J. Liu, Mohammad Saleh, Etienne Pot, Ben Goodrich, Ryan Sepassi, Lukasz Kaiser, Noam Shazeer (GoogleBrain)
Funding
Google Cloud Platform, European Research Council
Who added the Dataset to GEM?
Ronald Cardenas (University of Edinburgh) Laura Perez-Beltrachini (University of Edinburgh)
Dataset Structure
Data Fields
id: ID of the data exampletitle: Is the Wikipedia article's titleparagraphs: Is the ranked list of paragraphs from the set of crawled textssummary: Is constituted by a list of sentences together with their corresponding topic label
Example Instance
This is a truncated example from the animal setting:
{'gem_id': 'animal-train-1',
'gem_parent_id': 'animal-train-1',
'id': '2652',
'paragraphs': ["lytrosis (hulst) of louisiana vernon antoine brou jr. 2005. southern lepidopterists' news, 27: 7 ., ..."],
'references': ['lytrosis unitaria , the common lytrosis moth, is a species of moth of the geometridae family. it is found in north america, including arkansas, georgia, iowa , massachusetts, and wisconsin. the wingspan is about 50 mm. the larvae feed on rosa, crataegus, amelanchier, acer, quercus and viburnum species.'],
'summary': {'text': ['lytrosis unitaria , the common lytrosis moth , is a species of moth of the geometridae family .',
'it is found in north america , including arkansas , georgia , iowa , massachusetts , new hampshire , new jersey , new york , north carolina , ohio , oklahoma , ontario , pennsylvania , south carolina , tennessee , texas , virginia , west virginia and wisconsin .',
'the wingspan is about 50 mm .',
'the larvae feed on rosa , crataegus , amelanchier , acer , quercus and viburnum species . '],
'topic': [29, 20, 9, 8]},
'target': 'lytrosis unitaria , the common lytrosis moth, is a species of moth of the geometridae family. it is found in north america, including arkansas, georgia, iowa , massachusetts, and wisconsin. the wingspan is about 50 mm. the larvae feed on rosa, crataegus, amelanchier, acer, quercus and viburnum species.',
'title': 'lytrosis unitaria'}
Data Splits
Nb of instances in train/valid/test are 50,938/2,855/2,831
Splitting Criteria
The data was split i.i.d., i.e. uniformly split into training, validation, and test datasets.
Dataset in GEM
Rationale for Inclusion in GEM
Why is the Dataset in GEM?
Evaluation of models' performance on noisy (document, summary) pairs and long inputs. Evaluate models' capabilities to generalise and mitigate biases.
Similar Datasets
no
Unique Language Coverage
no
Ability that the Dataset measures
Capabilities to generalise, mitigate biases, factual correctness.
GEM-Specific Curation
Modificatied for GEM?
yes
GEM Modifications
annotations added
Modification Details
We provide topic labels for summary sentences.
Additional Splits?
no
Getting Started with the Task
Pointers to Resources
- Generating Wikipedia by Summarizing Long Sequences
- Generating Summaries with Topic Templates and Structured Convolutional Decoders
- Noisy Self-Knowledge Distillation for Text Summarization
And all references in these papers.
Previous Results
Previous Results
Measured Model Abilities
Capabilities to generalise, mitigate biases, factual correctness.
Metrics
ROUGE, BERT-Score, MoverScore, Other: Other Metrics
Other Metrics
- Abstract/Copy
- Factual accuracy based on the score of (Goodrich et al., 2019) and the relation extraction system of (Sorokin and Gurevych, 2017).
Proposed Evaluation
Human based are Question Answering and Ranking (Content, Fluency and Repetition)
Previous results available?
yes
Other Evaluation Approaches
Those listed above.
Relevant Previous Results
Generating Summaries with Topic Templates and Structured Convolutional Decoders https://arxiv.org/abs/1906.04687
Noisy Self-Knowledge Distillation for Text Summarization https://arxiv.org/abs/2009.07032
Dataset Curation
Original Curation
Original Curation Rationale
The dataset is a subset of the WikiSum (Liu et al., 2018) dataset focusing on summaries of entities in three domains (Film, Company, and Animal). It is multi-document summarisation where input-output pairs for each example entity are created as follows. The input is a set of paragraphs collected from i) documents in the Reference section of the entity's Wikipedia page plus ii) documents collected from the top ten search results after querying Google search engine with the entity name. The output summary is the Wikipedia abstract for the entity.
Communicative Goal
Generate descriptive summaries with specific domains, where certain topics are discussed and generally in specific orders.
Sourced from Different Sources
yes
Source Details
WikiSum (Liu et al., 2018)
Language Data
How was Language Data Obtained?
Other
Topics Covered
The dataset and task focuses on summaries for entities in three domains: Company, Film, and Animal.
Data Validation
not validated
Data Preprocessing
Summary sentences are associated with a topic label. There is a topic model for each domain.
Was Data Filtered?
not filtered
Structured Annotations
Additional Annotations?
automatically created
Annotation Service?
no
Annotation Values
Each summary sentences was annotated with a topic label. There is a topic model for each of the three domains. This was used to guide a hierarchical decoder.
Any Quality Control?
validated by data curators
Quality Control Details
Manual inspection of a sample of topics assigned to sentences. The number of topics was selected based on the performance of the summarisation model.
Consent
Any Consent Policy?
no
Justification for Using the Data
The dataset is base on Wikipedia and referenced and retrieved documents crawled from the Web.
Private Identifying Information (PII)
Contains PII?
unlikely
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?
yes
Links and Summaries of Analysis Work
This dataset is based on Wikipedia and thus biases analysis on other Wikipedia-based datasets are potentially true for WikiCatSum. For instance, see analysis for the ToTTo dataset here [1].
[1] Automatic Construction of Evaluation Suites for Natural Language Generation Datasets https://openreview.net/forum?id=CSi1eu_2q96
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