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multi_xscience / multi_xscience.py

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	# coding=utf-8
	# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
	#
	# 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.

	import json
	import os
	from typing import List

	import datasets

	from .bigbiohub import text2text_features
	from .bigbiohub import BigBioConfig
	from .bigbiohub import Tasks

	_LANGUAGES = ['English']
	_PUBMED = False
	_LOCAL = False
	_CITATION = """\
	@misc{https://doi.org/10.48550/arxiv.2010.14235,
	doi = {10.48550/ARXIV.2010.14235},

	url = {https://arxiv.org/abs/2010.14235},

	author = {Lu, Yao and Dong, Yue and Charlin, Laurent},

	keywords = {Computation and Language (cs.CL), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},

	title = {Multi-XScience: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles},

	publisher = {arXiv},

	year = {2020},

	copyright = {arXiv.org perpetual, non-exclusive license}
	}
	"""

	_DATASETNAME = "multi_xscience"
	_DISPLAYNAME = "Multi-XScience"

	_DESCRIPTION = """\
	Multi-document summarization is a challenging task for which there exists little large-scale datasets.
	We propose 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. Our work is inspired by extreme summarization,
	a dataset construction protocol that favours abstractive modeling approaches. Descriptive statistics and
	empirical results---using several state-of-the-art models trained on the Multi-XScience dataset---reveal t
	hat Multi-XScience is well suited for abstractive models.
	"""

	_HOMEPAGE = "https://github.com/yaolu/Multi-XScience"

	_LICENSE = 'MIT License'

	_URLS = {
	_DATASETNAME: [
	"https://github.com/yaolu/Multi-XScience/blob/master/data/train.json.gz?raw=true",
	"https://github.com/yaolu/Multi-XScience/blob/master/data/test.json.gz?raw=true",
	"https://github.com/yaolu/Multi-XScience/blob/master/data/val.json.gz?raw=true",
	],
	}

	_SUPPORTED_TASKS = [Tasks.PARAPHRASING, Tasks.SUMMARIZATION]

	_SOURCE_VERSION = "1.0.0"

	_BIGBIO_VERSION = "1.0.0"


	class MultiXScience(datasets.GeneratorBasedBuilder):
	"""
	Dataset for the EMNLP 2020 paper, Multi-XScience:
	A Large-scale Dataset for Extreme Multi-document Summarization
	of Scientific Articles.
	"""

	SOURCE_VERSION = datasets.Version(_SOURCE_VERSION)
	BIGBIO_VERSION = datasets.Version(_BIGBIO_VERSION)

	BUILDER_CONFIGS = [
	BigBioConfig(
	name="multi_xscience_source",
	version=SOURCE_VERSION,
	description="multi_xscience source schema",
	schema="source",
	subset_id="multi_xscience",
	),
	BigBioConfig(
	name="multi_xscience_bigbio_t2t",
	version=BIGBIO_VERSION,
	description="multi_xscienceBigBio schema",
	schema="bigbio_t2t",
	subset_id="multi_xscience",
	),
	]

	DEFAULT_CONFIG_NAME = "multi_xscience_source"

	def _info(self) -> datasets.DatasetInfo:

	if self.config.schema == "source":
	features = datasets.Features(
	{
	"aid": datasets.Value("string"),
	"mid": datasets.Value("string"),
	"abstract": datasets.Value("string"),
	"ref_abstract": datasets.Sequence(
	{
	"mid": datasets.Value("string"),
	"abstract": datasets.Value("string"),
	}
	),
	}
	)
	elif self.config.schema == "bigbio_t2t":
	features = text2text_features

	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	homepage=_HOMEPAGE,
	license=str(_LICENSE),
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager) -> List[datasets.SplitGenerator]:

	urls = _URLS[_DATASETNAME]
	data_dir = dl_manager.download_and_extract(urls)

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# Whatever you put in gen_kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": os.path.join(data_dir[0]).replace("\\", "/"),
	"split": "train",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={
	"filepath": os.path.join(data_dir[1]).replace("\\", "/"),
	"split": "test",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={
	"filepath": os.path.join(data_dir[2]).replace("\\", "/"),
	"split": "val",
	},
	),
	]

	# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`

	def _generate_examples(self, filepath, split):
	j_file = open(filepath, "r")
	j_file.seek(0)
	j_json = json.load(j_file)

	if self.config.schema == "source":
	for key, example in enumerate(j_json):
	yield key, {
	"aid": example["aid"],
	"mid": example["mid"],
	"abstract": example["abstract"],
	"ref_abstract": [
	{
	"mid": example["ref_abstract"][key]["mid"],
	"abstract": example["ref_abstract"][key]["abstract"],
	}
	for key in example["ref_abstract"].keys()
	],
	}

	elif self.config.schema == "bigbio_t2t":
	uid = 0

	for key, example in enumerate(j_json):
	uid += 1
	yield key, {
	"id": str(uid),
	"document_id": str(key),
	"text_1": example["abstract"],
	"text_2": " ".join(
	[e["abstract"] for e in example["ref_abstract"].values()]
	),
	"text_1_name": "Abstract of query paper",
	"text_2_name": "Cite abstracts",
	}

	j_file.close()