Datasets:
Tasks:
Summarization
Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
found
Annotations Creators:
found
Source Datasets:
original
ArXiv:
License:
File size: 3,941 Bytes
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# 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
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