Text · dataset · 2022
MeQSum
Listed in Hugging Face Datasets
Dataset for medical question summarization introduced in the ACL 2019 paper "On the Summarization of Consumer Health Questions".
Description
Question understanding is one of the main challenges in question answering. In real world applications, users often submit natural language questions that are longer than needed and include peripheral information that increases the complexity of the question, leading to substantially more false positives in answer retrieval.
In this paper, we study neural abstractive models for medical question summarization. We introduce the MeQSum corpus of 1,000 summarized consumer health questions.
Links
Where it is published
- Hugging Face dataset page huggingface.co/datasets/bigbio/meqsum ↗
landing page · from Hugging Face
Catalogue records · 1
- Hub API huggingface.co/api/datasets/bigbio/meqsum ↗
metadata API · from Hugging Face
Topics
- Stated by source
- text
- From keywords
- Computer Science & AI
Provenance · 1 source records, 9 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| Hugging Face Datasets | bigbio/meqsum | 14 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| access_level | source · Hugging Face | connector:huggingface@1.0.0 | /gated |
| concepts[field].local:field:computer-science-ai | mapping · Hugging Face | connector:huggingface@1.0.0 | |
| concepts[modality].hf_modality:text | source · Hugging Face | connector:huggingface@1.0.0 | |
| created_date | source · Hugging Face | connector:huggingface@1.0.0 | |
| description | source · Hugging Face | connector:huggingface@1.0.0 | /description |
| license_text | source · Hugging Face | connector:huggingface@1.0.0 | |
| publication_date | source · Hugging Face | connector:huggingface@1.0.0 | |
| title | source · Hugging Face | connector:huggingface@1.0.0 | /id |
| updated_date | source · Hugging Face | connector:huggingface@1.0.0 |