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README.md

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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.",
-}
-```
-

val.zip → document/pubmed-summarization-test.parquet

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vocab.zip → document/pubmed-summarization-validation.parquet

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pubmed-summarization.py

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-import json
-import os
-
-import datasets
-from datasets.tasks import TextClassification
-
-_CITATION = None
-
-
-_DESCRIPTION = """
- 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
-"""
-_CITATION = """\
-    @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.",
-}
-"""
-_ABSTRACT = "abstract"
-_ARTICLE = "article"
-
-class PubMedSummarizationConfig(datasets.BuilderConfig):
-    """BuilderConfig for PubMedSummarization."""
-
-    def __init__(self, **kwargs):
-        """BuilderConfig for PubMedSummarization.
-        Args:
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(PubMedSummarizationConfig, self).__init__(**kwargs)
-
-
-class PubMedSummarizationDataset(datasets.GeneratorBasedBuilder):
-    """PubMedSummarization Dataset."""
-    
-    _TRAIN_FILE = "train.zip"
-    _VAL_FILE = "val.zip"
-    _TEST_FILE = "test.zip"
-
-    BUILDER_CONFIGS = [
-        PubMedSummarizationConfig(
-            name="section",
-            version=datasets.Version("1.0.0"),
-            description="PubMed dataset for summarization, concat sections",
-        ),
-        PubMedSummarizationConfig(
-            name="document",
-            version=datasets.Version("1.0.0"),
-            description="PubMed dataset for summarization, document",
-        ),
-    ]
-
-    DEFAULT_CONFIG_NAME = "section"
-
-    def _info(self):
-        # Should return a datasets.DatasetInfo object
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    _ARTICLE: datasets.Value("string"),
-                    _ABSTRACT: datasets.Value("string"),
-                    #"id": datasets.Value("string"),
-                }
-            ),
-            supervised_keys=None,
-            homepage="https://github.com/armancohan/long-summarization",
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-
-        train_path = os.path.join(dl_manager.download_and_extract(self._TRAIN_FILE), "train.txt")
-        val_path = os.path.join(dl_manager.download_and_extract(self._VAL_FILE), "val.txt")
-        test_path = os.path.join(dl_manager.download_and_extract(self._TEST_FILE), "test.txt")
-        
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION, gen_kwargs={"filepath": val_path}
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}
-            ),
-        ]
-    
-    def _generate_examples(self, filepath):
-        """Generate PubMedSummarization examples."""
-        with open(filepath, encoding="utf-8") as f:
-            for id_, row in enumerate(f):
-                data = json.loads(row)
-
-                """
-                'article_id': str,
-                'abstract_text': List[str],
-                'article_text': List[str],
-                'section_names': List[str],
-                'sections': List[List[str]]
-                """
-                if self.config.name == "document":
-                    article = [d.strip() for d in data["article_text"]]
-                    article = " ".join(article)
-                else:
-                    article = [item.strip() for sublist in data["sections"] for item in sublist]
-                    article = " \n ".join(article)
-
-                abstract = [ab.replace("<S>", "").replace("</S>", "").strip() for ab in data["abstract_text"]]
-                abstract = " \n ".join(abstract)
-                yield id_, {"article": article, "abstract": abstract}

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