README.md

---
language:
- en
license: unknown
multilinguality:
- monolingual
pretty_name: MeQSum
size_categories:
- n<1K
source_datasets:
- original
task_categories:
- summarization
task_ids: []
paperswithcode_id: meqsum
tags:
- medical
---

# Dataset Card for MeQSum

## Table of Contents
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)

## Dataset Description

- **Homepage:** 
- **Repository:** https://github.com/abachaa/MeQSum
- **Paper:** [On the Summarization of Consumer Health Questions](https://aclanthology.org/P19-1215)
- **Leaderboard:**
- **Point of Contact:** [Asma Ben Abacha](mailto:[email protected])

### Dataset Summary

MeQSum corpus is a dataset for medical question summarization. It contains 1,000 summarized consumer health questions.

### Supported Tasks and Leaderboards

[More Information Needed]

### Languages

English (`en`).

## Dataset Structure

### Data Instances

```
{
  "CHQ": "SUBJECT: who and where to get cetirizine - D\\nMESSAGE: I need\\/want to know who manufscturs Cetirizine. My Walmart is looking for a new supply and are not getting the recent",
  "Summary": "Who manufactures cetirizine?",
  "File": "1-131188152.xml.txt"
}
```

### Data Fields

- `CHQ` (str): Consumer health question.
- `Summary` (str): Question summarization, i.e., condensed question expressing the minimum information required to find correct answers to the original question.
- `File` (str): Filename.

### Data Splits

The dataset consists of a single `train` split containing 1,000 examples.

## 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

If you use the MeQSum corpus, please cite:

```
@inproceedings{ben-abacha-demner-fushman-2019-summarization,
    title = "On the Summarization of Consumer Health Questions",
    author = "Ben Abacha, Asma  and
      Demner-Fushman, Dina",
    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/P19-1215",
    doi = "10.18653/v1/P19-1215",
    pages = "2228--2234",
    abstract = "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. We explore data augmentation methods and evaluate state-of-the-art neural abstractive models on this new task. In particular, we show that semantic augmentation from question datasets improves the overall performance, and that pointer-generator networks outperform sequence-to-sequence attentional models on this task, with a ROUGE-1 score of 44.16{\%}. We also present a detailed error analysis and discuss directions for improvement that are specific to question summarization.",
}
```

### Contributions

Thanks to [@albertvillanova](https://huggingface.co/albertvillanova) for adding this dataset.