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# 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(),
}
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