Datasets:
Tasks:
Summarization
Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
found
Annotations Creators:
found
Source Datasets:
original
ArXiv:
License:
| # coding=utf-8 | |
| # Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| # Lint as: python3 | |
| """Multi-XScience Dataset.""" | |
| import json | |
| import datasets | |
| _CITATION = """ | |
| @article{lu2020multi, | |
| title={Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles}, | |
| author={Lu, Yao and Dong, Yue and Charlin, Laurent}, | |
| journal={arXiv preprint arXiv:2010.14235}, | |
| year={2020} | |
| } | |
| """ | |
| _DESCRIPTION = """ | |
| Multi-XScience, a large-scale multi-document summarization dataset created from scientific articles. Multi-XScience introduces a challenging multi-document summarization task: writing the related-work section of a paper based on its abstract and the articles it references. | |
| """ | |
| _URL_TRAIN = "https://raw.githubusercontent.com/yaolu/Multi-XScience/master/data/train.json.gz" | |
| _URL_TEST = "https://raw.githubusercontent.com/yaolu/Multi-XScience/master/data/test.json.gz" | |
| _URL_VAL = "https://raw.githubusercontent.com/yaolu/Multi-XScience/master/data/val.json.gz" | |
| class MultiXScienceSum(datasets.GeneratorBasedBuilder): | |
| """ "Multi-XScience Dataset.""" | |
| VERSION = datasets.Version("1.1.0") | |
| def _info(selif): | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=datasets.Features( | |
| { | |
| "aid": datasets.Value("string"), | |
| "mid": datasets.Value("string"), | |
| "abstract": datasets.Value("string"), | |
| "related_work": datasets.Value("string"), | |
| "ref_abstract": datasets.Sequence( | |
| { | |
| "cite_N": datasets.Value("string"), | |
| "mid": datasets.Value("string"), | |
| "abstract": datasets.Value("string"), | |
| }, | |
| ), | |
| } | |
| ), | |
| supervised_keys=None, | |
| homepage="https://github.com/yaolu/Multi-XScience", | |
| citation=_CITATION, | |
| ) | |
| def _split_generators(self, dl_manager): | |
| """Returns SplitGenerators.""" | |
| train_path = dl_manager.download_and_extract(_URL_TRAIN) | |
| test_path = dl_manager.download_and_extract(_URL_TEST) | |
| val_path = dl_manager.download_and_extract(_URL_VAL) | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| gen_kwargs={"path": train_path}, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TEST, | |
| gen_kwargs={"path": test_path}, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.VALIDATION, | |
| gen_kwargs={"path": val_path}, | |
| ), | |
| ] | |
| def _generate_examples(self, path=None): | |
| """Yields examples.""" | |
| with open(path, encoding="utf-8") as f: | |
| data = json.load(f) | |
| f.close() | |
| for idx, el in enumerate(data): | |
| cite_n = list(el["ref_abstract"].keys()) | |
| cite_n_mid = [el["ref_abstract"][cite]["mid"] for cite in cite_n] | |
| cite_n_abstract = [el["ref_abstract"][cite]["abstract"] for cite in cite_n] | |
| tmp = {"cite_N": cite_n, "mid": cite_n_mid, "abstract": cite_n_abstract} | |
| d = el.copy() | |
| d["ref_abstract"] = tmp | |
| yield idx, d | |