wiki_cat_sumSummarization

wiki_cat_sum

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

Github

paper

Arxiv

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)

Quick-Use

Contact Name

If known, provide the name of at least one person the reader can contact for questions about the dataset.

Laura Perez-Beltrachini

Multilingual?

Is the dataset multilingual?

no

Covered Languages

What languages/dialects are covered in the dataset?

English

License

What is the license of the dataset?

cc-by-sa-3.0: Creative Commons Attribution Share Alike 3.0 Unported

Communicative Goal

Provide a short description of the communicative goal of a model trained for this task on this dataset.

Summarise the most important facts of a given entity in the Film, Company, and Animal domains from a cluster of related documents.

Additional Annotations?

Does the dataset have additional annotations for each instance?

automatically created

Contains PII?

Does the source language data likely contain Personal Identifying Information about the data creators or subjects?

unlikely

Dataset Overview

  • Where to find the Data and its Documentation

  • Languages and Intended Use

  • Credit

  • Dataset Structure

Where to find the Data and its Documentation

Webpage

What is the webpage for the dataset (if it exists)?

Github

Download

What is the link to where the original dataset is hosted?

Website

Paper

What is the link to the paper describing the dataset (open access preferred)?

Arxiv

BibTex

Provide the BibTex-formatted reference for the dataset. Please use the correct published version (ACL anthology, etc.) instead of google scholar created 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

If known, provide the name of at least one person the reader can contact for questions about the dataset.

Laura Perez-Beltrachini

Contact Email

If known, provide the email of at least one person the reader can contact for questions about the dataset.

lperez@ed.ac.uk

Has a Leaderboard?

Does the dataset have an active leaderboard?

no

Languages and Intended Use

Multilingual?

Is the dataset multilingual?

no

Covered Languages

What languages/dialects are covered in the dataset?

English

License

What is the license of the dataset?

cc-by-sa-3.0: Creative Commons Attribution Share Alike 3.0 Unported

Intended Use

What is the intended use of the dataset?

Research on multi-document abstractive summarisation.

Primary Task

What primary task does the dataset support?

Summarization

Communicative Goal

Provide a short description of the communicative goal of a model trained for this task on this dataset.

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)

In what kind of organization did the dataset curation happen?

industry, academic

Curation Organization(s)

Name the organization(s).

Google Cloud Platform, University of Edinburgh

Dataset Creators

Who created the original dataset? List the people involved in collecting the dataset and their affiliation(s).

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

Who funded the data creation?

Google Cloud Platform, European Research Council

Who added the Dataset to GEM?

Who contributed to the data card and adding the dataset to GEM? List the people+affiliations involved in creating this data card and who helped integrate this dataset into GEM.

Ronald Cardenas (University of Edinburgh) Laura Perez-Beltrachini (University of Edinburgh)

Dataset Structure

Data Fields

List and describe the fields present in the dataset.

  • id: ID of the data example
  • title: Is the Wikipedia article's title
  • paragraphs: Is the ranked list of paragraphs from the set of crawled texts
  • summary: Is constituted by a list of sentences together with their corresponding topic label
Example Instance

Provide a JSON formatted example of a typical instance in the dataset.

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

Describe and name the splits in the dataset if there are more than one.

Nb of instances in train/valid/test are 50,938/2,855/2,831

Splitting Criteria

Describe any criteria for splitting the data, if used. If there are differences between the splits (e.g., if the training annotations are machine-generated and the dev and test ones are created by humans, or if different numbers of annotators contributed to each example), describe them here.

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

  • GEM-Specific Curation

  • Getting Started with the Task

Rationale for Inclusion in GEM

Why is the Dataset in GEM?

What does this dataset contribute toward better generation evaluation and why is it part of GEM?

Evaluation of models' performance on noisy (document, summary) pairs and long inputs. Evaluate models' capabilities to generalise and mitigate biases.

Similar Datasets

Do other datasets for the high level task exist?

no

Unique Language Coverage

Does this dataset cover other languages than other datasets for the same task?

no

Ability that the Dataset measures

What aspect of model ability can be measured with this dataset?

Capabilities to generalise, mitigate biases, factual correctness.

GEM-Specific Curation

Modificatied for GEM?

Has the GEM version of the dataset been modified in any way (data, processing, splits) from the original curated data?

yes

GEM Modifications

What changes have been made to he original dataset?

annotations added

Modification Details

For each of these changes, described them in more details and provided the intended purpose of the modification

We provide topic labels for summary sentences.

Additional Splits?

Does GEM provide additional splits to the dataset?

no

Getting Started with the Task

Pointers to Resources

Getting started with in-depth research on the task. Add relevant pointers to resources that researchers can consult when they want to get started digging deeper into the task.

And all references in these papers.

Previous Results

  • Previous Results

Previous Results

Measured Model Abilities

What aspect of model ability can be measured with this dataset?

Capabilities to generalise, mitigate biases, factual correctness.

Metrics

What metrics are typically used for this task?

ROUGE, BERT-Score, MoverScore, Other: Other Metrics

Other Metrics

Definitions of 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

List and describe the purpose of the metrics and evaluation methodology (including human evaluation) that the dataset creators used when introducing this task.

Human based are Question Answering and Ranking (Content, Fluency and Repetition)

Previous results available?

Are previous results available?

yes

Other Evaluation Approaches

What evaluation approaches have others used?

Those listed above.

Relevant Previous Results

What are the most relevant previous results for this task/dataset?

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

  • Language Data

  • Structured Annotations

  • Consent

  • Private Identifying Information (PII)

  • Maintenance

Original Curation

Original Curation Rationale

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

What was the communicative goal?

Generate descriptive summaries with specific domains, where certain topics are discussed and generally in specific orders.

Sourced from Different Sources

Is the dataset aggregated from different data sources?

yes

Source Details

List the sources (one per line)

WikiSum (Liu et al., 2018)

Language Data

How was Language Data Obtained?

How was the language data obtained?

Other

Topics Covered

Does the language in the dataset focus on specific topics? How would you describe them?

The dataset and task focuses on summaries for entities in three domains: Company, Film, and Animal.

Data Validation

Was the text validated by a different worker or a data curator?

not validated

Data Preprocessing

How was the text data pre-processed? (Enter N/A if the text was not pre-processed)

Summary sentences are associated with a topic label. There is a topic model for each domain.

Was Data Filtered?

Were text instances selected or filtered?

not filtered

Structured Annotations

Additional Annotations?

Does the dataset have additional annotations for each instance?

automatically created

Annotation Service?

Was an annotation service used?

no

Annotation Values

Purpose and values for each annotation

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?

Quality control measures?

validated by data curators

Quality Control Details

Describe the quality control measures that were taken.

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?

Was there a consent policy involved when gathering the data?

no

Justification for Using the Data

If not, what is the justification for reusing the data?

The dataset is base on Wikipedia and referenced and retrieved documents crawled from the Web.

Private Identifying Information (PII)

Contains PII?

Does the source language data likely contain Personal Identifying Information about the data creators or subjects?

unlikely

Any PII Identification?

Did the curators use any automatic/manual method to identify PII in the dataset?

no identification

Maintenance

Any Maintenance Plan?

Does the original dataset have a maintenance plan?

no

Broader Social Context

  • Previous Work on the Social Impact of the Dataset

  • Impact on Under-Served Communities

  • Discussion of Biases

Previous Work on the Social Impact of the Dataset

Usage of Models based on the Data

Are you aware of cases where models trained on the task featured in this dataset ore related tasks have been used in automated systems?

no

Impact on Under-Served Communities

Addresses needs of underserved Communities?

Does this dataset address the needs of communities that are traditionally underserved in language technology, and particularly language generation technology? Communities may be underserved for exemple because their language, language variety, or social or geographical context is underepresented in NLP and NLG resources (datasets and models).

no

Discussion of Biases

Any Documented Social Biases?

Are there documented social biases in the dataset? Biases in this context are variations in the ways members of different social categories are represented that can have harmful downstream consequences for members of the more disadvantaged group.

yes

Links and Summaries of Analysis Work

Provide links to and summaries of works analyzing these biases.

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

  • Known Technical Limitations

PII Risks and Liability

Licenses

Copyright Restrictions on the Dataset

Based on your answers in the Intended Use part of the Data Overview Section, which of the following best describe the copyright and licensing status of the dataset?

public domain

Copyright Restrictions on the Language Data

Based on your answers in the Language part of the Data Curation Section, which of the following best describe the copyright and licensing status of the underlying language data?

public domain

Known Technical Limitations