| --- |
| language: |
| - en |
| multilinguality: |
| - monolingual |
| size_categories: |
| - 100K<n<1M |
| task_categories: |
| - summarization |
| - text-generation |
| task_ids: [] |
| tags: |
| - conditional-text-generation |
| dataset_info: |
| - config_name: document |
| features: |
| - name: article |
| dtype: string |
| - name: abstract |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 2236406736 |
| num_examples: 119924 |
| - name: validation |
| num_bytes: 126510743 |
| num_examples: 6633 |
| - name: test |
| num_bytes: 126296182 |
| num_examples: 6658 |
| download_size: 1154975484 |
| dataset_size: 2489213661 |
| - config_name: section |
| features: |
| - name: article |
| dtype: string |
| - name: abstract |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 2257744955 |
| num_examples: 119924 |
| - name: validation |
| num_bytes: 127711559 |
| num_examples: 6633 |
| - name: test |
| num_bytes: 127486937 |
| num_examples: 6658 |
| download_size: 1163165290 |
| dataset_size: 2512943451 |
| configs: |
| - config_name: document |
| data_files: |
| - split: train |
| path: document/train-* |
| - split: validation |
| path: document/validation-* |
| - split: test |
| path: document/test-* |
| - config_name: section |
| data_files: |
| - split: train |
| path: section/train-* |
| - split: validation |
| path: section/validation-* |
| - split: test |
| path: section/test-* |
| default: true |
| --- |
| |
| # 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.", |
| } |
| ``` |
|
|
|
|
