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language:
- en
license: mit
pretty_name: Cancer Abstract Dataset
size_categories:
- 1K\<n\<10K
tags:
- biomedical
- oncology
- cancer
- text-classification
- nlp
- graph-neural-networks
- document-classification
task_categories:
- text-classification
---
# Cancer Abstract Dataset
## Dataset Summary
The **Cancer Abstract Dataset** is a curated collection of biomedical
research abstracts categorized by cancer type. It was developed to
support research in document classification, low-resource biomedical
NLP, and graph-based deep learning approaches.
This dataset was introduced in the following publication:
> Hossain, E., Nuzhat, T., Masum, S., et al.\
> **R-GAT: cancer document classification leveraging graph-based
> residual network for scenarios with limited data.**\
> *Scientific Reports*, 16, 6582 (2026).\
> https://doi.org/10.1038/s41598-026-39894-6
------------------------------------------------------------------------
## Dataset Description
This dataset contains categorized research abstracts related to major
cancer types. It is suitable for:
- Biomedical text classification
- Topic modeling
- Low-resource learning experiments
- Graph-based NLP methods
- Transformer-based fine-tuning
- Benchmarking uncertainty-aware LLMs
------------------------------------------------------------------------
## Dataset Structure
### Total Samples
**1,874 abstracts**
### Format
CSV (Comma-Separated Values)
### Fields
Field Description
------------ -----------------------------
`Abstract` Full research abstract text
`Category` Cancer type label
### Categories
- `Lung_Cancer`
- `Thyroid_Cancer`
- `Colon_Cancer`
- `Generic`
------------------------------------------------------------------------
## Example Usage
``` python
from datasets import load_dataset
dataset = load_dataset("EliasHossain/CancerAbstracts")
print(dataset["train"][0])
```
------------------------------------------------------------------------
## Intended Use
The dataset is intended for:
- Supervised text classification
- Graph neural network research
- Transformer-based fine-tuning
- Biomedical NLP benchmarking
- Limited-data learning evaluation
This dataset is **not intended for clinical decision-making**.
------------------------------------------------------------------------
## Data Collection and Processing
Abstracts were curated and categorized for research purposes in
oncology-related document classification experiments. Standard
preprocessing steps were applied to ensure formatting consistency.
No personally identifiable information (PII) or protected health
information (PHI) is included.
------------------------------------------------------------------------
## Citation
If you use this dataset, please cite:
``` bibtex
@article{hossain2026rgat,
title={R-GAT: cancer document classification leveraging graph-based residual network for scenarios with limited data},
author={Hossain, Elias and Nuzhat, Tasfia and Masum, S. and others},
journal={Scientific Reports},
volume={16},
pages={6582},
year={2026},
doi={10.1038/s41598-026-39894-6}
}
```
------------------------------------------------------------------------
## Contributors
- **Elias Hossain**\
Mississippi State University, USA
- **Tasfia Nuzhat**\
Chittagong Independent University, Bangladesh
------------------------------------------------------------------------
## License
MIT License
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