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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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_DESCRIPTION = """ |
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GovReport dataset for summarization. |
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From paper: Efficient Attentions for Long Document Summarization" by L. Huang et al. |
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See: https://arxiv.org/pdf/2104.02112.pdf |
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See: https://github.com/luyang-huang96/LongDocSum |
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""" |
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_CITATION = """\ |
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@misc{huang2021efficient, |
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title={Efficient Attentions for Long Document Summarization}, |
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author={Luyang Huang and Shuyang Cao and Nikolaus Parulian and Heng Ji and Lu Wang}, |
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year={2021}, |
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eprint={2104.02112}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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} |
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""" |
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_ABSTRACT = "summary" |
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_ARTICLE = "report" |
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class GovReportSummarizationConfig(datasets.BuilderConfig): |
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"""BuilderConfig for GovReportSummarization.""" |
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def __init__(self, **kwargs): |
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"""BuilderConfig for GovReportSummarization. |
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Args: |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(GovReportSummarizationConfig, self).__init__(**kwargs) |
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class GovReportSummarizationDataset(datasets.GeneratorBasedBuilder): |
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"""GovReportSummarization Dataset.""" |
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_TRAIN_FILE = "train.zip" |
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_VAL_FILE = "valid.zip" |
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_TEST_FILE = "test.zip" |
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BUILDER_CONFIGS = [ |
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GovReportSummarizationConfig( |
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name="document", |
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version=datasets.Version("1.0.0"), |
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description="GovReport dataset for summarization, document", |
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), |
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] |
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DEFAULT_CONFIG_NAME = "document" |
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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/luyang-huang96/LongDocSum", |
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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 = os.path.join(dl_manager.download_and_extract(self._TRAIN_FILE), "train.txt") |
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val_path = os.path.join(dl_manager.download_and_extract(self._VAL_FILE), "valid.txt") |
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test_path = os.path.join(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 GovReportSummarization 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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report = data["report"] |
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summary = data["summary"] |
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yield id_, {"report": report, "summary": summary} |
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