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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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WCEP10 dataset for summarization. |
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From paper: "A Large-Scale Multi-Document Summarization Dataset from the Wikipedia |
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Current Events Portal" by D. Gholipour et al." |
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From paper: "PRIMER: Pyramid-based Masked Sentence Pre-training for Multi-document |
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Summarization" by W. Xiao et al." |
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""" |
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_CITATION = """\ |
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@article{DBLP:journals/corr/abs-2005-10070, |
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author = {Demian Gholipour Ghalandari and |
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Chris Hokamp and |
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Nghia The Pham and |
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John Glover and |
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Georgiana Ifrim}, |
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title = {A Large-Scale Multi-Document Summarization Dataset from the Wikipedia |
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Current Events Portal}, |
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journal = {CoRR}, |
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volume = {abs/2005.10070}, |
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year = {2020}, |
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url = {https://arxiv.org/abs/2005.10070}, |
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eprinttype = {arXiv}, |
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eprint = {2005.10070}, |
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timestamp = {Fri, 22 May 2020 16:21:28 +0200}, |
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biburl = {https://dblp.org/rec/journals/corr/abs-2005-10070.bib}, |
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bibsource = {dblp computer science bibliography, https://dblp.org} |
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} |
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@article{DBLP:journals/corr/abs-2110-08499, |
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author = {Wen Xiao and |
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Iz Beltagy and |
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Giuseppe Carenini and |
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Arman Cohan}, |
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title = {{PRIMER:} Pyramid-based Masked Sentence Pre-training for Multi-document |
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Summarization}, |
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journal = {CoRR}, |
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volume = {abs/2110.08499}, |
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year = {2021}, |
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url = {https://arxiv.org/abs/2110.08499}, |
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eprinttype = {arXiv}, |
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eprint = {2110.08499}, |
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timestamp = {Fri, 22 Oct 2021 13:33:09 +0200}, |
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biburl = {https://dblp.org/rec/journals/corr/abs-2110-08499.bib}, |
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bibsource = {dblp computer science bibliography, https://dblp.org} |
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} |
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""" |
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_ABSTRACT = "summary" |
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_ARTICLE = "document" |
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class WCEP10SummarizationConfig(datasets.BuilderConfig): |
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"""BuilderConfig for WCEP10Summarization.""" |
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def __init__(self, **kwargs): |
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"""BuilderConfig for WCEP10Summarization. |
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Args: |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(WCEP10SummarizationConfig, self).__init__(**kwargs) |
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class WCEP10SummarizationDataset(datasets.GeneratorBasedBuilder): |
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"""WCEP10Summarization 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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WCEP10SummarizationConfig( |
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name="newline", |
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version=datasets.Version("1.0.0"), |
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description="WCEP10 dataset for summarization, concat sections", |
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), |
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WCEP10SummarizationConfig( |
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name="roberta", |
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version=datasets.Version("1.0.0"), |
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description="WCEP10 dataset for summarization, document", |
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), |
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WCEP10SummarizationConfig( |
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name="bert", |
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version=datasets.Version("1.0.0"), |
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description="WCEP10 dataset for summarization, document", |
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), |
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WCEP10SummarizationConfig( |
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name="list", |
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version=datasets.Version("1.0.0"), |
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description="WCEP10 dataset for summarization, document", |
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), |
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] |
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DEFAULT_CONFIG_NAME = "roberta" |
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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.Sequence(datasets.Value("string")) if self.config.name == "list" else 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/allenai/PRIMER", |
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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), "val.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 WCEP10Summarization examples.""" |
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if self.config.name == "newline": |
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join_ = "\n" |
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elif self.config.name == "roberta": |
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join_ = "</s>" |
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elif self.config.name == "bert": |
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join_ = "[SEP]" |
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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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'summary': str, |
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'document': List[str], |
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""" |
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document = data["document"] |
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if self.config.name != "list": |
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document = join_.join(document) |
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summary = data["summary"] |
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yield id_, {"document": document, "summary": summary} |
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