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"""CiteSum dataset""" |
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import hashlib |
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import os |
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import datasets |
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logger = datasets.logging.get_logger(__name__) |
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_HOMEPAGE = "https://github.com/morningmoni/CiteSum" |
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_DESCRIPTION = """\ |
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Citation Text-guided Scientific Extreme Summarization and Low-resource Domain Adaptation |
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CiteSum contains TLDR summaries for scientific papers from their citation texts without human annotation. |
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CiteSum is around 30 times larger than the previous human-curated dataset SciTLDR. |
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""" |
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_CITATION = """\ |
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@misc{https://doi.org/10.48550/arxiv.2205.06207, |
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doi = {10.48550/ARXIV.2205.06207}, |
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url = {https://arxiv.org/abs/2205.06207}, |
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author = {Mao, Yuning and Zhong, Ming and Han, Jiawei}, |
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keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, |
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title = {CiteSum: Citation Text-guided Scientific Extreme Summarization and Low-resource Domain Adaptation}, |
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publisher = {arXiv}, |
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year = {2022}, |
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copyright = {Creative Commons Attribution 4.0 International} |
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} |
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""" |
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_DOWNLOAD_URL = "https://drive.google.com/file/d/1ndHCREXGSPnDUNllladh9qCtayqbXAfJ" |
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class CiteSumConfig(datasets.BuilderConfig): |
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"""BuilderConfig for CiteSum.""" |
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def __init__(self, **kwargs): |
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"""BuilderConfig for CiteSum. |
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Args: |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super().__init__(**kwargs) |
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class CiteSum(datasets.GeneratorBasedBuilder): |
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"""CiteSum summarization dataset.""" |
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BUILDER_CONFIGS = [CiteSumConfig(name="citesum", description="Plain text")] |
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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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"src": datasets.Value("string"), |
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"tgt": datasets.Value("string"), |
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"paper_id": datasets.Value("string"), |
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"title": datasets.Value("string"), |
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"discipline": { |
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"venue": datasets.Value("string"), |
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"journal": datasets.Value("string"), |
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"mag_field_of_study": datasets.features.Sequence( |
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datasets.Value("string") |
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), |
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}, |
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} |
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), |
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supervised_keys=None, |
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homepage=_HOMEPAGE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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dl_paths = dl_manager.download(_DOWNLOAD_URL) |
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return [ |
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datasets.SplitGenerator( |
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name=split, |
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gen_kwargs={ |
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"urls_file": dl_paths[split], |
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"files_per_archive": [ |
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dl_manager.iter_archive(dl_paths["cnn_stories"]), |
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dl_manager.iter_archive(dl_paths["dm_stories"]), |
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], |
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}, |
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) |
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for split in [ |
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datasets.Split.TRAIN, |
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datasets.Split.VALIDATION, |
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datasets.Split.TEST, |
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] |
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] |
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def _generate_examples(self, urls_file, files_per_archive): |
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urls = _get_url_hashes(urls_file) |
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idx = 0 |
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for files in files_per_archive: |
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for path, file in files: |
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hash_from_path = _get_hash_from_path(path) |
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if hash_from_path in urls: |
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article, highlights = _get_art_abs(file, self.config.version) |
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if not article or not highlights: |
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continue |
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yield idx, { |
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_ARTICLE: article, |
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_HIGHLIGHTS: highlights, |
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"id": hash_from_path, |
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} |
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idx += 1 |
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