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"""ConcluGen Dataset""" |
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import json |
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import datasets |
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_CITATION = """\ |
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@inproceedings{syed:2021, |
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author = {Shahbaz Syed and |
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Khalid Al Khatib and |
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Milad Alshomary and |
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Henning Wachsmuth and |
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Martin Potthast}, |
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editor = {Chengqing Zong and |
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Fei Xia and |
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Wenjie Li and |
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Roberto Navigli}, |
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title = {Generating Informative Conclusions for Argumentative Texts}, |
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booktitle = {Findings of the Association for Computational Linguistics: {ACL/IJCNLP} |
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2021, Online Event, August 1-6, 2021}, |
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pages = {3482--3493}, |
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publisher = {Association for Computational Linguistics}, |
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year = {2021}, |
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url = {https://doi.org/10.18653/v1/2021.findings-acl.306}, |
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doi = {10.18653/v1/2021.findings-acl.306} |
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} |
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""" |
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_DESCRIPTION = """\ |
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The ConcluGen corpus is constructed for the task of argument summarization. It consists of 136,996 pairs of argumentative texts and their conclusions collected from the ChangeMyView subreddit, a web portal for argumentative discussions on controversial topics. |
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The corpus has three variants: aspects, topics, and targets. Each variation encodes the corresponding information via control codes. These provide additional argumentative knowledge for generating more informative conclusions. |
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""" |
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_HOMEPAGE = "https://zenodo.org/record/4818134" |
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_LICENSE = "https://creativecommons.org/licenses/by/4.0/legalcode" |
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_REPO = "https://huggingface.co/datasets/webis/conclugen/resolve/main" |
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_URLS = { |
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'base_train': f"{_REPO}/base_train.jsonl", |
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'base_validation': f"{_REPO}/base_validation.jsonl", |
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'base_test': f"{_REPO}/base_test.jsonl", |
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'aspects_train': f"{_REPO}/aspects_train.jsonl", |
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'aspects_validation': f"{_REPO}/aspects_validation.jsonl", |
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'aspects_test': f"{_REPO}/aspects_test.jsonl", |
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'targets_train': f"{_REPO}/targets_train.jsonl", |
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'targets_validation': f"{_REPO}/targets_validation.jsonl", |
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'targets_test': f"{_REPO}/targets_test.jsonl", |
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'topic_train': f"{_REPO}/topic_train.jsonl", |
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'topic_validation': f"{_REPO}/topic_validation.jsonl", |
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'topic_test': f"{_REPO}/topic_test.jsonl" |
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} |
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class ConcluGen(datasets.GeneratorBasedBuilder): |
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"""382,545 arguments crawled from debate portals""" |
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VERSION = datasets.Version("1.1.0") |
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BUILDER_CONFIGS = [ |
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datasets.BuilderConfig(name="base", version=VERSION, description="The base version of the dataset with no argumentative knowledge."), |
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datasets.BuilderConfig(name="aspects", version=VERSION, description="Variation with argument aspects encoded."), |
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datasets.BuilderConfig(name="targets", version=VERSION, description="Variation with conclusion targets encoded."), |
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datasets.BuilderConfig(name="topic", version=VERSION, description="Variation with discussion topic encoded."), |
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] |
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DEFAULT_CONFIG_NAME = "base" |
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def _info(self): |
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features = datasets.Features( |
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{ |
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"argument": datasets.Value("string"), |
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"conclusion": datasets.Value("string"), |
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"id": datasets.Value("string") |
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} |
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) |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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supervised_keys=None, |
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homepage=_HOMEPAGE, |
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license=_LICENSE, |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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"""Returns SplitGenerators.""" |
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train_file = dl_manager.download(_URLS[self.config.name+"_train"]) |
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validation_file = dl_manager.download(_URLS[self.config.name+"_validation"]) |
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test_file = dl_manager.download(_URLS[self.config.name+"_test"]) |
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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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"data_file": train_file, |
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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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"data_file": validation_file, |
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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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"data_file": test_file, |
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}, |
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) |
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] |
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def _generate_examples(self, data_file): |
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""" Yields examples as (key, example) tuples. """ |
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with open(data_file, encoding="utf-8") as f: |
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for row in f: |
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data = json.loads(row) |
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id_ = data['id'] |
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yield id_, { |
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"argument": data['argument'], |
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"conclusion": data["conclusion"], |
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"id": id_ |
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} |
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