Create multi_document_summarization.py
Browse files- multi_document_summarization.py +102 -0
multi_document_summarization.py
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# coding=utf-8
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# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# Lint as: python3
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"""Multi-Document Dataset."""
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import json
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import datasets
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_CITATION = """
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@article{lu2020multi,
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title={Multi-Document: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles},
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author={Arka Das, India},
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journal={arXiv preprint arXiv:2010.14235},
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year={2022}
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}
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"""
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_DESCRIPTION = """
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Multi-Document, a large-scale multi-document summarization dataset created from scientific articles. Multi-Document introduces a challenging multi-document summarization task: writing the related-work section of a paper based on its abstract and the articles it references.
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"""
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_URL_TRAIN = "https://github.com/arka0821/multi_document_summarization/data/train.json.gz"
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_URL_TEST = "https://github.com/arka0821/multi_document_summarization/data/test.json.gz"
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_URL_VAL = "https://github.com/arka0821/multi_document_summarization/data/val.json.gz"
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class MultiDocumentSum(datasets.GeneratorBasedBuilder):
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""" "Multi-Document Dataset."""
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VERSION = datasets.Version("1.1.0")
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def _info(selif):
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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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"id": datasets.Value("string"),
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"docs": datasets.Sequence(
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{
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"id": datasets.Value("string"),
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"text": datasets.Value("string")
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},
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),
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"summary": 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/arka0821/multi_document_summarization",
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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_path = dl_manager.download_and_extract(_URL_TRAIN)
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test_path = dl_manager.download_and_extract(_URL_TEST)
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val_path = dl_manager.download_and_extract(_URL_VAL)
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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={"path": train_path},
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),
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datasets.SplitGenerator(
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name=datasets.Split.TEST,
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gen_kwargs={"path": test_path},
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),
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datasets.SplitGenerator(
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name=datasets.Split.VALIDATION,
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gen_kwargs={"path": val_path},
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),
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]
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def _generate_examples(self, path=None):
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"""Yields examples."""
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with open(path, encoding="utf-8") as f:
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data = json.load(f)
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f.close()
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for idx, el in enumerate(data):
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cite_n = list(el["ref_abstract"].keys())
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cite_n_mid = [el["ref_abstract"][cite]["mid"] for cite in cite_n]
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cite_n_abstract = [el["ref_abstract"][cite]["abstract"] for cite in cite_n]
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tmp = {"cite_N": cite_n, "mid": cite_n_mid, "abstract": cite_n_abstract}
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d = el.copy()
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d["summary"] = tmp
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yield idx, d
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