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import json |
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import os |
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import datasets |
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from datasets.tasks import TextClassification |
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_CITATION = None |
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_DESCRIPTION = """ |
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PubMed dataset for summarization. |
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From paper: A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents" by A. Cohan et al. |
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See: https://aclanthology.org/N18-2097.pdf |
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See: https://github.com/armancohan/long-summarization |
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""" |
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_CITATION = """\ |
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@inproceedings{cohan-etal-2018-discourse, |
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title = "A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents", |
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author = "Cohan, Arman and |
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Dernoncourt, Franck and |
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Kim, Doo Soon and |
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Bui, Trung and |
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Kim, Seokhwan and |
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Chang, Walter and |
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Goharian, Nazli", |
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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)", |
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month = jun, |
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year = "2018", |
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address = "New Orleans, Louisiana", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/N18-2097", |
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doi = "10.18653/v1/N18-2097", |
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pages = "615--621", |
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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.", |
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} |
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""" |
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_ABSTRACT = "abstract" |
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_ARTICLE = "article" |
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class PubMedSummarizationConfig(datasets.BuilderConfig): |
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"""BuilderConfig for PatentClassification.""" |
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def __init__(self, **kwargs): |
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"""BuilderConfig for PubMedSummarization. |
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Args: |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(PubMedSummarizationConfig, self).__init__(**kwargs) |
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class PubMedSummarizationDataset(datasets.GeneratorBasedBuilder): |
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"""PubMedSummarization Dataset.""" |
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_TRAIN_FILE = "train.zip" |
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_VAL_FILE = "val.zip" |
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_TEST_FILE = "test.zip" |
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BUILDER_CONFIGS = [ |
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PubMedSummarizationConfig( |
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name="pubmed", |
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version=datasets.Version("1.0.0"), |
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description="PubMed dataset for summarization", |
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), |
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] |
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DEFAULT_CONFIG_NAME = "pubmed" |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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_ARTICLE: datasets.Value("string"), |
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_ABSTRACT: datasets.Value("string"), |
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} |
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), |
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supervised_keys=None, |
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homepage="https://github.com/armancohan/long-summarization", |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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train_path = dl_manager.download_and_extract(self._TRAIN_FILE) + "/train.txt" |
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val_path = dl_manager.download_and_extract(self._VAL_FILE) + "/val.txt" |
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test_path = dl_manager.download_and_extract(self._TEST_FILE) + "/test.txt" |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path} |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, gen_kwargs={"filepath": val_path} |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, gen_kwargs={"filepath": test_path} |
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), |
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] |
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def _generate_examples(self, filepath): |
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"""Generate PubMedSummarization examples.""" |
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with open(filepath, encoding="utf-8") as f: |
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for id_, row in enumerate(f): |
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data = json.loads(row) |
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""" |
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'article_id': str, |
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'abstract_text': List[str], |
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'article_text': List[str], |
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'section_names': List[str], |
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'sections': List[List[str]] |
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""" |
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article = data["article_text"] |
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abstract = data["abstract_text"] |
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yield id_, {"article": ' '.join(article), "abstract": ' '.join(abstract)} |
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