
# 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](https://arxiv.org/abs/2210.09932)
- **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
](https://arxiv.org/abs/2210.09932)" . 

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)](https://plos.org/), whereas eLife articles are derived from the [eLife](https://elifesciences.org/) 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](https://paperswithcode.com/sota/lay-summarization-on-plos) and [eLife](https://paperswithcode.com/sota/lay-summarization-on-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`: a `string` feature.
- `section_headings`: a `string` feature.
- `keywords`: a `string` feature.
- `title` : a `string` feature.
- `year` : a `string` feature.
- `summary`: a `string` feature.


#### elife

- `article`: a `string` feature.
- `section_headings`: a `string` feature.
- `keywords`: a `string` feature.
- `title` : a `string` feature.
- `year` : a `string` feature.
- `summary`: a `string` 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
```
task_categories:summarization annotations_creators:found language_creators:found multilinguality:monolingual size_categories:10K<n<100K size_categories:1K<n<10K source_datasets:original language:en license:cc-by-4.0 abstractive-summarization scientific-papers lay-summarization PLOS eLife arxiv:2210.09932 region:us
{"annotations_creators": ["found"], "language_creators": ["found"], "language": ["en"], "license": ["cc-by-4.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K", "1K<n<10K"], "source_datasets": ["original"], "task_categories": ["summarization"], "task_ids": [], "pretty_name": "ScientificLaySummarisation", "tags": ["abstractive-summarization", "scientific-papers", "lay-summarization", "PLOS", "eLife"]}
TAGS
#task_categories-summarization #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc-by-4.0 #abstractive-summarization #scientific-papers #lay-summarization #PLOS #eLife #arxiv-2210.09932 #region-us 

Dataset Card for "scientific\_lay\_summarisation"
=================================================


* Repository: URL
* 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.


#### 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.


### Data Fields


The data fields are the same among all splits.


#### plos


* 'article': a 'string' feature.
* 'section\_headings': a 'string' feature.
* 'keywords': a 'string' feature.
* 'title' : a 'string' feature.
* 'year' : a 'string' feature.
* 'summary': a 'string' feature.


#### elife


* 'article': a 'string' feature.
* 'section\_headings': a 'string' feature.
* 'keywords': a 'string' feature.
* 'title' : a 'string' feature.
* 'year' : a 'string' feature.
* 'summary': a 'string' feature.


### Data Splits



Dataset Creation
----------------


### Curation Rationale


### Source Data


#### Initial Data Collection and Normalization


#### Who are the source language producers?


### Annotations


#### Annotation process


#### Who are the annotators?


### Personal and Sensitive Information


Considerations for Using the Data
---------------------------------


### Social Impact of Dataset


### Discussion of Biases


### Other Known Limitations


Additional Information
----------------------


### Dataset Curators


### Licensing Information



### 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. #### 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. ### Data Fields


The data fields are the same among all splits. #### plos


* 'article': a 'string' feature.
* 'section\_headings': a 'string' feature.
* 'keywords': a 'string' feature.
* 'title' : a 'string' feature.
* 'year' : a 'string' feature.
* 'summary': a 'string' feature. #### elife


* 'article': a 'string' feature.
* 'section\_headings': a 'string' feature.
* 'keywords': a 'string' feature.
* 'title' : a 'string' feature.
* 'year' : a 'string' feature.
* 'summary': a 'string' feature. ### Data Splits



Dataset Creation
---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information


Considerations for Using the Data
--------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations


Additional Information
---------------------- ### Dataset Curators ### Licensing Information
TAGS
#task_categories-summarization #annotations_creators-found #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #size_categories-1K<n<10K #source_datasets-original #language-English #license-cc-by-4.0 #abstractive-summarization #scientific-papers #lay-summarization #PLOS #eLife #arxiv-2210.09932 #region-us 
 ### 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. #### 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. ### Data Fields


The data fields are the same among all splits. #### plos


* 'article': a 'string' feature.
* 'section\_headings': a 'string' feature.
* 'keywords': a 'string' feature.
* 'title' : a 'string' feature.
* 'year' : a 'string' feature.
* 'summary': a 'string' feature. #### elife


* 'article': a 'string' feature.
* 'section\_headings': a 'string' feature.
* 'keywords': a 'string' feature.
* 'title' : a 'string' feature.
* 'year' : a 'string' feature.
* 'summary': a 'string' feature. ### Data Splits



Dataset Creation
---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization #### Who are the source language producers? ### Annotations #### Annotation process #### Who are the annotators? ### Personal and Sensitive Information


Considerations for Using the Data
--------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations


Additional Information
---------------------- ### Dataset Curators ### Licensing Information