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Dataset Card for "scientific_lay_summarisation"
- Repository: https://github.com/TGoldsack1/Corpora_for_Lay_Summarisation
- Paper: Making Science Simple: Corpora for the Lay Summarisation of Scientific Literature
- Size of downloaded dataset files: 850.44 MB
- Size of the generated dataset: 1.32 GB
- Total amount of disk used: 2.17 GB
Dataset Summary
This repository contains the PLOS and eLife datasets, introduced in the EMNLP 2022 paper "Making Science Simple: Corpora for the Lay Summarisation of Scientific Literature " .
Each dataset contains full biomedical research articles paired with expert-written lay summaries (i.e., non-technical summaries). PLOS articles are derived from various journals published by the Public Library of Science (PLOS), whereas eLife articles are derived from the eLife journal. More details/analyses on the content of each dataset are provided in the paper.
Both "elife" and "plos" have 6 features:
- "article": the body of the document (including the abstract), sections separated by "/n".
- "section_headings": the title of each section, separated by "/n".
- "keywords": keywords describing the topic of the article, separated by "/n".
- "title": the title of the article.
- "year": the year the article was published.
- "summary": the lay summary of the document.
Note: The format of both datasets differs from that used in the original repository (given above) in order to make them compatible with the run_summarization.py
script of Transformers. Specifically, sentence tokenization is removed via " ".join(text), and the abstract and article sections, previously lists of sentences, are combined into a single string
feature ("article") with each section separated by "\n". For the sentence-tokenized version of the dataset, please use the original git repository.
Supported Tasks and Leaderboards
Papers with code - PLOS and eLife.
Languages
English
Dataset Structure
Data Instances
plos
- Size of downloaded dataset files: 425.22 MB
- Size of the generated dataset: 1.05 GB
- Total amount of disk used: 1.47 GB
An example of 'train' looks as follows.
This example was too long and was cropped:
{
"summary": "In the kidney , structures known as nephrons are responsible for collecting metabolic waste . Nephrons are composed of a ...",
"article": "Kidney function depends on the nephron , which comprises a 'blood filter , a tubule that is subdivided into functionally ...",
"section_headings": "Abstract\nIntroduction\nResults\nDiscussion\nMaterials and Methods'",
"keywords": "developmental biology\ndanio (zebrafish)\nvertebrates\nteleost fishes\nnephrology",
"title": "The cdx Genes and Retinoic Acid Control the Positioning and Segmentation of the Zebrafish Pronephros",
"year": "2007"
}
elife
- Size of downloaded dataset files: 425.22 MB
- Size of the generated dataset: 275.99 MB
- Total amount of disk used: 1.47 MB
An example of 'train' looks as follows.
This example was too long and was cropped:
{
"summary": "In the USA , more deaths happen in the winter than the summer . But when deaths occur varies greatly by sex , age , cause of ...",
"article": "In temperate climates , winter deaths exceed summer ones . However , there is limited information on the timing and the ...",
"section_headings": "Abstract\nIntroduction\nResults\nDiscussion\nMaterials and methods",
"keywords": "epidemiology and global health",
"title": "National and regional seasonal dynamics of all-cause and cause-specific mortality in the USA from 1980 to 2016",
"year": "2018"
}
Data Fields
The data fields are the same among all splits.
plos
article
: astring
feature.section_headings
: astring
feature.keywords
: astring
feature.title
: astring
feature.year
: astring
feature.summary
: astring
feature.
elife
article
: astring
feature.section_headings
: astring
feature.keywords
: astring
feature.title
: astring
feature.year
: astring
feature.summary
: astring
feature.
Data Splits
name | train | validation | test |
---|---|---|---|
plos | 24773 | 1376 | 1376 |
elife | 4346 | 241 | 241 |
Dataset Creation
Curation Rationale
[More Information Needed]
Source Data
Initial Data Collection and Normalization
[More Information Needed]
Who are the source language producers?
[More Information Needed]
Annotations
Annotation process
[More Information Needed]
Who are the annotators?
[More Information Needed]
Personal and Sensitive Information
[More Information Needed]
Considerations for Using the Data
Social Impact of Dataset
[More Information Needed]
Discussion of Biases
[More Information Needed]
Other Known Limitations
[More Information Needed]
Additional Information
Dataset Curators
[More Information Needed]
Licensing Information
[More Information Needed]
Citation Information
"Making Science Simple: Corpora for the Lay Summarisation of Scientific Literature"
Tomas Goldsack, Zhihao Zhang, Chenghua Lin, Carolina Scarton
EMNLP 2022
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