task_categories:summarization size_categories:1K<n<10K size_categories:10K<n<100K language:en license:apache-2.0 medical region:us
@inproceedings{kotonya-toni-2020-explainable,
    title = "Explainable Automated Fact-Checking for Public Health Claims",
    author = "Kotonya, Neema  and
    Toni, Francesca",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2020.emnlp-main.623",
    pages = "7740--7754",
}
---
license: apache-2.0
task_categories:
- summarization
language:
- en
tags:
- medical
size_categories:
- 1K<n<10K
- 10K<n<100K
---

# `clupubhealth`

The `CLUPubhealth` dataset is based on the [PUBHEALTH fact-checking dataset](https://github.com/neemakot/Health-Fact-Checking).

The PUBHEALTH dataset contains claims, explanations, and main texts. The explanations function as vetted summaries of the main texts. The CLUPubhealth dataset repurposes these fields into summaries and texts for use in training Summarization models such as Facebook's BART. 

There are currently 4 dataset configs which can be called, each has three splits (see Usage):

### `clupubhealth/mini`

This config includes only 200 samples per split. This is mostly used in testing scripts when small sets are desirable.

### `clupubhealth/base`

This is the base dataset which includes the full PUBHEALTH set, sans False samples. The `test` split is a shortened version which includes only 200 samples. This allows for faster eval steps during trianing.

### `clupubhealth/expanded`

Where the base `train` split contains 5,078 data points, this expanded set includes 62,163 data points. ChatGPT was used to generate new versions of the summaries in the base set. After GPT expansion a total of 72,498 were generated, however, this was shortened to ~62k after samples with poor BERTScores were eliminated.

### `clupubhealth/test`

This config has the full `test` split with ~1200 samples. Used for post-training evaluation.

## USAGE

To use the CLUPubhealth dataset use the `datasets` library:

```python
from datasets import load_dataset

data = load_dataset("clu-ling/clupubhealth", "base")

# Where the accepted extensions are the configs: `mini`, `base`, `expanded`, `test`
```