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ccdv/pubmed-summarization

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… See the full description on the dataset page: https://huggingface.co/datasets/ccdv/pubmed-summarization.

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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:

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.",
}