Datasets:
annotations_creators:
- none
language_creators:
- unknown
language:
- en
license:
- cc-by-4.0
multilinguality:
- unknown
size_categories:
- unknown
source_datasets:
- original
task_categories:
- text2text-generation
task_ids:
- text-simplification
pretty_name: cochrane-simplification
Dataset Card for GEM/cochrane-simplification
Dataset Description
- Homepage: https://github.com/AshOlogn/Paragraph-level-Simplification-of-Medical-Texts
- Repository: https://github.com/AshOlogn/Paragraph-level-Simplification-of-Medical-Texts
- Paper: https://aclanthology.org/2021.naacl-main.395/
- Leaderboard: N/A
- Point of Contact: Ashwin Devaraj
Link to Main Data Card
You can find the main data card on the GEM Website.
Dataset Summary
Cochrane is an English dataset for paragraph-level simplification of medical texts. Cochrane is a database of systematic reviews of clinical questions, many of which have summaries in plain English targeting readers without a university education. The dataset comprises about 4,500 of such pairs.
You can load the dataset via:
import datasets
data = datasets.load_dataset('GEM/cochrane-simplification')
The data loader can be found here.
website
paper
authors
Ashwin Devaraj (The University of Texas at Austin), Iain J. Marshall (King's College London), Byron C. Wallace (Northeastern University), Junyi Jessy Li (The University of Texas at Austin)
Dataset Overview
Where to find the Data and its Documentation
Webpage
Download
Paper
BibTex
@inproceedings{devaraj-etal-2021-paragraph,
title = "Paragraph-level Simplification of Medical Texts",
author = "Devaraj, Ashwin and
Marshall, Iain and
Wallace, Byron and
Li, Junyi Jessy",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.395",
doi = "10.18653/v1/2021.naacl-main.395",
pages = "4972--4984",
}
Contact Name
Ashwin Devaraj
Contact Email
Has a Leaderboard?
no
Languages and Intended Use
Multilingual?
no
Covered Languages
English
License
cc-by-4.0: Creative Commons Attribution 4.0 International
Intended Use
The intended use of this dataset is to train models that simplify medical text at the paragraph level so that it may be more accessible to the lay reader.
Primary Task
Simplification
Communicative Goal
A model trained on this dataset can be used to simplify medical texts to make them more accessible to readers without medical expertise.
Credit
Curation Organization Type(s)
academic
Curation Organization(s)
The University of Texas at Austin, King's College London, Northeastern University
Dataset Creators
Ashwin Devaraj (The University of Texas at Austin), Iain J. Marshall (King's College London), Byron C. Wallace (Northeastern University), Junyi Jessy Li (The University of Texas at Austin)
Funding
National Institutes of Health (NIH) grant R01-LM012086, National Science Foundation (NSF) grant IIS-1850153, Texas Advanced Computing Center (TACC) computational resources
Who added the Dataset to GEM?
Ashwin Devaraj (The University of Texas at Austin)
Dataset Structure
Data Fields
gem_id: string, a unique identifier for the exampledoi: string, DOI identifier for the Cochrane review from which the example was generatedsource: string, an excerpt from an abstract of a Cochrane reviewtarget: string, an excerpt from the plain-language summary of a Cochrane review that roughly aligns with the source text
Example Instance
{
"gem_id": "gem-cochrane-simplification-train-766",
"doi": "10.1002/14651858.CD002173.pub2",
"source": "Of 3500 titles retrieved from the literature, 24 papers reporting on 23 studies could be included in the review. The studies were published between 1970 and 1997 and together included 1026 participants. Most were cross-over studies. Few studies provided sufficient information to judge the concealment of allocation. Four studies provided results for the percentage of symptom-free days. Pooling the results did not reveal a statistically significant difference between sodium cromoglycate and placebo. For the other pooled outcomes, most of the symptom-related outcomes and bronchodilator use showed statistically significant results, but treatment effects were small. Considering the confidence intervals of the outcome measures, a clinically relevant effect of sodium cromoglycate cannot be excluded. The funnel plot showed an under-representation of small studies with negative results, suggesting publication bias. There is insufficient evidence to be sure about the efficacy of sodium cromoglycate over placebo. Publication bias is likely to have overestimated the beneficial effects of sodium cromoglycate as maintenance therapy in childhood asthma.",
"target": "In this review we aimed to determine whether there is evidence for the effectiveness of inhaled sodium cromoglycate as maintenance treatment in children with chronic asthma. Most of the studies were carried out in small groups of patients. Furthermore, we suspect that not all studies undertaken have been published. The results show that there is insufficient evidence to be sure about the beneficial effect of sodium cromoglycate compared to placebo. However, for several outcome measures the results favoured sodium cromoglycate."
}
Data Splits
train: 3568 examplesvalidation: 411 examplestest: 480 examples
Dataset in GEM
Rationale for Inclusion in GEM
Why is the Dataset in GEM?
This dataset is the first paragraph-level simplification dataset published (as prior work had primarily focused on simplifying individual sentences). Furthermore, this dataset is in the medical domain, which is an especially useful domain for text simplification.
Similar Datasets
no
Ability that the Dataset measures
This dataset measures the ability for a model to simplify paragraphs of medical text through the omission non-salient information and simplification of medical jargon.
GEM-Specific Curation
Modificatied for GEM?
no
Additional Splits?
no
Getting Started with the Task
Previous Results
Previous Results
Measured Model Abilities
This dataset measures the ability for a model to simplify paragraphs of medical text through the omission non-salient information and simplification of medical jargon.
Metrics
Other: Other Metrics, BLEU
Other Metrics
SARI measures the quality of text simplification
Previous results available?
yes
Relevant Previous Results
The paper which introduced this dataset trained BART models (pretrained on XSum) with unlikelihood training to produce simplification models achieving maximum SARI and BLEU scores of 40 and 43 respectively.
Dataset Curation
Original Curation
Sourced from Different Sources
no
Language Data
Data Validation
not validated
Was Data Filtered?
not filtered
Structured Annotations
Additional Annotations?
none
Annotation Service?
no
Consent
Any Consent Policy?
no
Private Identifying Information (PII)
Contains PII?
yes/very likely
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?
yes
Details on how Dataset Addresses the Needs
This dataset can be used to simplify medical texts that may otherwise be inaccessible to those without medical training.
Discussion of Biases
Any Documented Social Biases?
unsure
Are the Language Producers Representative of the Language?
The dataset was generated from abstracts and plain-language summaries of medical literature reviews that were written by medical professionals and thus does was not generated by people representative of the entire English-speaking population.
Considerations for Using the Data
PII Risks and Liability
Licenses
Known Technical Limitations
Technical Limitations
The main limitation of this dataset is that the information alignment between the abstract and plain-language summary is often rough, so the plain-language summary may contain information that isn't found in the abstract. Furthermore, the plain-language targets often contain formulaic statements like "this evidence is current to [month][year]" not found in the abstracts. Another limitation is that some plain-language summaries do not simplify the technical abstracts very much and still contain medical jargon.
Unsuited Applications
The main pitfall to look out for is errors in factuality. Simplification work so far has not placed a strong emphasis on the logical fidelity of model generations with the input text, and the paper introducing this dataset does not explore modeling techniques to combat this. These kinds of errors are especially pernicious in the medical domain, and the models introduced in the paper do occasionally alter entities like disease and medication names.