task_categories:summarization task_categories:text-generation multilinguality:monolingual size_categories:100K<n<1M language:en conditional-text-generation region:us
PubMed dataset for summarization.
 From paper: A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents" by A. Cohan et al.
 See: https://aclanthology.org/N18-2097.pdf 
 See: https://github.com/armancohan/long-summarization
    @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.",
}
---
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.",
}
```

