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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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MediaSum dataset for summarization. |
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From paper: "MediaSum: A Large-scale Media Interview Dataset for Dialogue Summarization" by C. Zhu et al." |
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""" |
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_CITATION = """\ |
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@article{zhu2021mediasum, |
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title={MediaSum: A Large-scale Media Interview Dataset for Dialogue Summarization}, |
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author={Zhu, Chenguang and Liu, Yang and Mei, Jie and Zeng, Michael}, |
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journal={arXiv preprint arXiv:2103.06410}, |
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year={2021} |
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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 MediaSumSummarizationConfig(datasets.BuilderConfig): |
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"""BuilderConfig for MediaSumSummarization.""" |
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def __init__(self, **kwargs): |
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"""BuilderConfig for MediaSumSummarization. |
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Args: |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(MediaSumSummarizationConfig, self).__init__(**kwargs) |
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class MediaSumSummarizationDataset(datasets.GeneratorBasedBuilder): |
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"""MediaSumSummarization Dataset.""" |
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_TRAIN_FILE = "train_data.zip" |
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_VAL_FILE = "val_data.zip" |
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_TEST_FILE = "test_data.zip" |
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BUILDER_CONFIGS = [ |
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MediaSumSummarizationConfig( |
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name="newline", |
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version=datasets.Version("1.0.0"), |
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description="MediaSum dataset for summarization, concat sections", |
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), |
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MediaSumSummarizationConfig( |
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name="roberta", |
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version=datasets.Version("1.0.0"), |
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description="MediaSum dataset for summarization, document", |
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), |
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MediaSumSummarizationConfig( |
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name="bert", |
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version=datasets.Version("1.0.0"), |
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description="MediaSum dataset for summarization, document", |
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), |
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MediaSumSummarizationConfig( |
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name="list", |
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version=datasets.Version("1.0.0"), |
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description="MediaSum 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/zcgzcgzcg1/MediaSum", |
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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_data.txt" |
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val_path = dl_manager.download_and_extract(self._VAL_FILE) + "/val_data.txt" |
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test_path = dl_manager.download_and_extract(self._TEST_FILE) + "/test_data.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 MediaSumSummarization 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["utt"] |
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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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