Datasets:
Tasks:
Summarization
Languages:
English
Multilinguality:
monolingual
Size Categories:
n<1K
Language Creators:
found
Annotations Creators:
crowdsourced
Source Datasets:
original
License:
File size: 3,395 Bytes
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# 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
"""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}
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