PubMed dataset for summarization

Dataset for summarization of long documents.
Adapted from this repo.
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 script from Transformers if you add this line to the summarization_name_mapping variable:

"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.

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.",
}