Datasets:

arka0821
/

multi_document_summarization

# 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-Document Dataset."""


import json

import datasets


_CITATION = """
@article{lu2020multi,
  title={Multi-Document: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles},
  author={Arka Das, India},
  journal={arXiv preprint arXiv:2010.14235},
  year={2022}
}
"""

_DESCRIPTION = """
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.
"""

_URL_TRAIN = "https://github.com/arka0821/multi_document_summarization/data/train.json.gz"
_URL_TEST = "https://github.com/arka0821/multi_document_summarization/data/test.json.gz"
_URL_VAL = "https://github.com/arka0821/multi_document_summarization/data/val.json.gz"


class MultiDocumentSum(datasets.GeneratorBasedBuilder):
    """ "Multi-Document Dataset."""

    VERSION = datasets.Version("1.1.0")

    def _info(selif):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "docs": datasets.Sequence(
                        {
                            "id": datasets.Value("string"),
                            "text": datasets.Value("string")
                        },
                    ),
                    "summary": datasets.Value("string"),
                }
            ),
            supervised_keys=None,
            homepage="https://github.com/arka0821/multi_document_summarization",
            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["summary"] = tmp
            yield idx, d

	# 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-Document Dataset."""


	import json

	import datasets


	_CITATION = """
	@article{lu2020multi,
	title={Multi-Document: A Large-scale Dataset for Extreme Multi-document Summarization of Scientific Articles},
	author={Arka Das, India},
	journal={arXiv preprint arXiv:2010.14235},
	year={2022}
	}
	"""

	_DESCRIPTION = """
	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.
	"""

	_URL_TRAIN = "https://github.com/arka0821/multi_document_summarization/data/train.json.gz"
	_URL_TEST = "https://github.com/arka0821/multi_document_summarization/data/test.json.gz"
	_URL_VAL = "https://github.com/arka0821/multi_document_summarization/data/val.json.gz"


	class MultiDocumentSum(datasets.GeneratorBasedBuilder):
	""" "Multi-Document Dataset."""

	VERSION = datasets.Version("1.1.0")

	def _info(selif):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"id": datasets.Value("string"),
	"docs": datasets.Sequence(
	{
	"id": datasets.Value("string"),
	"text": datasets.Value("string")
	},
	),
	"summary": datasets.Value("string"),
	}
	),
	supervised_keys=None,
	homepage="https://github.com/arka0821/multi_document_summarization",
	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["summary"] = tmp
	yield idx, d