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import json |
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import os |
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import datasets |
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_CITATION = """\ |
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@inproceedings{sun-etal-2021-d2s, |
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title = "{D}2{S}: Document-to-Slide Generation Via Query-Based Text Summarization", |
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author = "Sun, Edward and |
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Hou, Yufang and |
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Wang, Dakuo and |
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Zhang, Yunfeng and |
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Wang, Nancy X. R.", |
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booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", |
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month = June, |
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year = "2021", |
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address = "Online", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2021.naacl-main.111", |
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doi = "10.18653/v1/2021.naacl-main.111", |
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pages = "1405--1418", |
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} |
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""" |
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_DESCRIPTION = """\ |
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SciDuet is the first publicaly available dataset for the challenging task of document2slides generation, |
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The dataset integrated into GEM is the ACL portion of the whole dataset described in "https://aclanthology.org/2021.naacl-main.111.pdf". |
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It contains the full Dev and Test sets, and a portion of the Train dataset. |
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We additionally create a challenge dataset in which the slide titles do not match with the |
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section headers of the corresponding paper. |
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Note that although we cannot release the whole training dataset due to copyright issues, researchers can still |
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use our released data procurement code from https://github.com/IBM/document2slides |
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to generate the training dataset from the online ICML/NeurIPS anthologies. |
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In the released dataset, the original papers and slides (both are in PDF format) are carefully processed by a combination of PDF/Image processing tookits. |
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The text contents from multiple slides that correspond to the same slide title are mreged. |
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""" |
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class SciDuetConfig(datasets.BuilderConfig): |
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"""BuilderConfig for SciDuet.""" |
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def __init__(self, **kwargs): |
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"""BuilderConfig for SciDuet. |
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Args: |
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**kwargs: keyword arguments forwarded to super. |
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""" |
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super(SciDuetConfig, self).__init__(**kwargs) |
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class SciDuet(datasets.GeneratorBasedBuilder): |
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VERSION = datasets.Version("1.0.0") |
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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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"gem_id": datasets.Value("string"), |
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"paper_id": datasets.Value("string"), |
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"paper_title": datasets.Value("string"), |
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"paper_abstract": datasets.Value("string"), |
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"paper_content": datasets.features.Sequence({ |
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"paper_content_id": datasets.Value("int32"), |
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"paper_content_text": datasets.Value("string"), |
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}), |
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"paper_headers": datasets.features.Sequence({ |
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"paper_header_number": datasets.Value("string"), |
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"paper_header_content": datasets.Value("string"), |
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}), |
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"slide_id": datasets.Value("string"), |
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"slide_title": datasets.Value("string"), |
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"slide_content_text": datasets.Value("string"), |
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"target": datasets.Value("string"), |
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"references": [datasets.Value("string")], |
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} |
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), |
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supervised_keys=None, |
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license="Apache License 2.0", |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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_URL = "https://huggingface.co/datasets/GEM/SciDuet/" |
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_URLs = { |
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"train": "train.json", |
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"validation": "validation.json", |
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"test": "test.json", |
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"challenge_set": "challenge_woSectionHeader.json", |
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} |
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downloaded_files = dl_manager.download_and_extract(_URLs) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"filepath": downloaded_files["train"], |
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"split": "train", |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={ |
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"filepath": downloaded_files["validation"], |
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"split": "validation", |
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}, |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={ |
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"filepath": downloaded_files["test"], |
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"split": "test", |
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}, |
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), |
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] + [ |
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datasets.SplitGenerator( |
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name="challenge_woSectionHeader", |
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gen_kwargs={ |
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"filepath": downloaded_files["challenge_set"], |
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"split": "challenge_woSectionHeader", |
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}, |
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), |
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] |
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def _generate_examples(self, filepath, split): |
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"""Yields examples.""" |
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with open(filepath, encoding="utf-8") as f: |
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data = json.load(f)["data"] |
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for item in data: |
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gem_id = item["gem_id"] |
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paper_id = item["paper_id"] |
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paper_title = item["paper_title"] |
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paper_abstract = item["paper"]["abstract"] |
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paper_content_ids = [text["id"] for text in item["paper"]["text"]] |
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paper_content_texts = [text["string"] for text in item["paper"]["text"]] |
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paper_header_numbers = [header["n"] for header in item["paper"]["headers"]] |
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paper_header_contents = [header["section"] for header in item["paper"]["headers"]] |
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for j in item["slides"]: |
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id_ = gem_id + "#" + "paper-" + paper_id + "#" + "slide-" + str(j) |
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slide_title = item["slides"][j]["title"] |
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slide_content_text = '\n'.join(item["slides"][j]["text"]) |
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yield id_, { |
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"gem_id": id_, |
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"paper_id": paper_id, |
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"paper_title": paper_title, |
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"paper_abstract": paper_abstract, |
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"paper_content": {"paper_content_id":paper_content_ids, "paper_content_text":paper_content_texts}, |
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"paper_headers": {"paper_header_number":paper_header_numbers, "paper_header_content":paper_header_contents}, |
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"slide_id": id_, |
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"slide_title": slide_title, |
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"slide_content_text": slide_content_text, |
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"target": slide_content_text, |
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"references": [] if split == "train" else [slide_content_text], |
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} |
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