Datasets:
opinosis

	# 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
	#
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	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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	# Lint as: python3
	"""Opinosis Opinion Dataset."""


	import os

	import datasets


	_CITATION = """
	@inproceedings{ganesan2010opinosis,
	title={Opinosis: a graph-based approach to abstractive summarization of highly redundant opinions},
	author={Ganesan, Kavita and Zhai, ChengXiang and Han, Jiawei},
	booktitle={Proceedings of the 23rd International Conference on Computational Linguistics},
	pages={340--348},
	year={2010},
	organization={Association for Computational Linguistics}
	}
	"""

	_DESCRIPTION = """
	The Opinosis Opinion Dataset consists of sentences extracted from reviews for 51 topics.
	Topics and opinions are obtained from Tripadvisor, Edmunds.com and Amazon.com.
	"""

	_URL = "https://github.com/kavgan/opinosis-summarization/raw/master/OpinosisDataset1.0_0.zip"

	_REVIEW_SENTS = "review_sents"
	_SUMMARIES = "summaries"


	class Opinosis(datasets.GeneratorBasedBuilder):
	"""Opinosis Opinion Dataset."""

	VERSION = datasets.Version("1.0.0")

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	_REVIEW_SENTS: datasets.Value("string"),
	_SUMMARIES: datasets.features.Sequence(datasets.Value("string")),
	}
	),
	supervised_keys=(_REVIEW_SENTS, _SUMMARIES),
	homepage="http://kavita-ganesan.com/opinosis/",
	citation=_CITATION,
	)

	def _split_generators(self, dl_manager):
	"""Returns SplitGenerators."""
	extract_path = dl_manager.download_and_extract(_URL)
	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={"path": extract_path},
	),
	]

	def _generate_examples(self, path=None):
	"""Yields examples."""
	topics_path = os.path.join(path, "topics")
	filenames = sorted(os.listdir(topics_path))
	for filename in filenames:
	file_path = os.path.join(topics_path, filename)
	topic_name = filename.split(".txt")[0]
	with open(file_path, "rb") as src_f:
	input_data = src_f.read().decode("latin-1")
	summaries_path = os.path.join(path, "summaries-gold", topic_name)
	summary_lst = []
	for summ_filename in sorted(os.listdir(summaries_path)):
	file_path = os.path.join(summaries_path, summ_filename)
	with open(file_path, "rb") as tgt_f:
	data = tgt_f.read().strip().decode("latin-1")
	summary_lst.append(data)
	summary_data = summary_lst
	yield filename, {_REVIEW_SENTS: input_data, _SUMMARIES: summary_data}