Datasets:

google-research-datasets
/

aquamuse

	# coding=utf-8
	# Copyright 2020 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.
	"""AQuaMuSe is a novel scalable approach to automatically mine dual query based multi-document summarization datasets for extractive and abstractive summaries using question answering dataset (Google Natural Questions) and large document corpora (Common Crawl)"""

	from __future__ import absolute_import, division, print_function

	import os
	from os import listdir
	from os.path import isfile, join

	import tensorflow as tf

	import datasets


	_CITATION = """\
	@misc{kulkarni2020aquamuse,
	title={AQuaMuSe: Automatically Generating Datasets for Query-Based Multi-Document Summarization},
	author={Sayali Kulkarni and Sheide Chammas and Wan Zhu and Fei Sha and Eugene Ie},
	year={2020},
	eprint={2010.12694},
	archivePrefix={arXiv},
	primaryClass={cs.CL}
	}
	"""

	_DESCRIPTION = """AQuaMuSe is a novel scalable approach to automatically mine dual query based multi-document summarization datasets for extractive and abstractive summaries using question answering dataset (Google Natural Questions) and large document corpora (Common Crawl)"""

	_HOMEPAGE = "https://github.com/google-research-datasets/aquamuse"

	_LICENSE = ""

	zipped_data_url = "https://github.com/google-research-datasets/aquamuse/raw/main/v2/aquamuse_v2.zip"


	class Aquamuse(datasets.GeneratorBasedBuilder):
	"""Dataset for Query-based Multi-Document Summarization"""

	VERSION = datasets.Version("2.3.0")

	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name="abstractive", version=VERSION, description="Abstractive query-based multi-document summarization"
	),
	datasets.BuilderConfig(
	name="extractive", version=VERSION, description="Extractive query-based multi-document summarization"
	),
	]

	# DEFAULT_CONFIG_NAME = "abstractive" # It's not mandatory to have a default configuration. Just use one if it make sense.

	def _info(self):
	features = datasets.Features(
	{
	"query": datasets.Value("string"),
	"input_urls": datasets.Sequence(datasets.Value("string")),
	"target": datasets.Value("string"),
	}
	)

	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	supervised_keys=None,
	homepage=_HOMEPAGE,
	license=_LICENSE,
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""

	if self.config.name == "abstractive":
	data_dir = dl_manager.download_and_extract(zipped_data_url)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": os.path.join(data_dir, "v2.3/abstractive/train/"),
	"split": "train",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": os.path.join(data_dir, "v2.3/abstractive/test/"),
	"split": "test",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": os.path.join(data_dir, "v2.3/abstractive/dev/"),
	"split": "dev",
	},
	),
	]

	else:
	data_dir = dl_manager.download_and_extract(zipped_data_url)
	print(data_dir)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": os.path.join(data_dir, "v2.3/extractive/train/"),
	"split": "train",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": os.path.join(data_dir, "v2.3/extractive/test/"),
	"split": "test",
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	# These kwargs will be passed to _generate_examples
	gen_kwargs={
	"filepath": os.path.join(data_dir, "v2.3/extractive/dev/"),
	"split": "dev",
	},
	),
	]

	def _generate_examples(self, filepath, split):
	""" Yields examples. """
	filepath = [join(filepath, f) for f in listdir(filepath) if isfile(join(filepath, f))]
	filepath = sorted(filepath)
	raw_dataset = tf.data.TFRecordDataset(filepath)
	for id_, raw_record in enumerate(raw_dataset):
	example = tf.train.Example()
	example.ParseFromString(raw_record.numpy())
	yield id_, {
	"query": example.features.feature["query"].bytes_list.value[0].decode(),
	"input_urls": example.features.feature["input_urls"].bytes_list.value[0].decode().split("<EOD>"),
	"target": example.features.feature["target"].bytes_list.value[0].decode(),
	}