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---
language:
- en
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
task_categories:
- summarization
- text-generation
task_ids: []
tags:
- conditional-text-generation
---
# PubMed dataset for summarization
Dataset for summarization of long documents.\
Adapted from this [repo](https://github.com/armancohan/long-summarization).\
Note that original data are pre-tokenized so this dataset returns " ".join(text) and add "\n" for paragraphs. \
This dataset is compatible with the [`run_summarization.py`](https://github.com/huggingface/transformers/tree/master/examples/pytorch/summarization) script from Transformers if you add this line to the `summarization_name_mapping` variable:
```python
"ccdv/pubmed-summarization": ("article", "abstract")
```
### Data Fields
- `id`: paper id
- `article`: a string containing the body of the paper
- `abstract`: a string containing the abstract of the paper
### Data Splits
This dataset has 3 splits: _train_, _validation_, and _test_. \
Token counts are white space based.
| Dataset Split | Number of Instances | Avg. tokens |
| ------------- | --------------------|:----------------------|
| Train | 119,924 | 3043 / 215 |
| Validation | 6,633 | 3111 / 216 |
| Test | 6,658 | 3092 / 219 |
# Cite original article
```
@inproceedings{cohan-etal-2018-discourse,
title = "A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents",
author = "Cohan, Arman and
Dernoncourt, Franck and
Kim, Doo Soon and
Bui, Trung and
Kim, Seokhwan and
Chang, Walter and
Goharian, Nazli",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-2097",
doi = "10.18653/v1/N18-2097",
pages = "615--621",
abstract = "Neural abstractive summarization models have led to promising results in summarizing relatively short documents. We propose the first model for abstractive summarization of single, longer-form documents (e.g., research papers). Our approach consists of a new hierarchical encoder that models the discourse structure of a document, and an attentive discourse-aware decoder to generate the summary. Empirical results on two large-scale datasets of scientific papers show that our model significantly outperforms state-of-the-art models.",
}
```
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