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gigaword / gigaword.py
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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
"""Gigaword summarization dataset."""
import os
import datasets
_CITATION = """
@article{graff2003english,
title={English gigaword},
author={Graff, David and Kong, Junbo and Chen, Ke and Maeda, Kazuaki},
journal={Linguistic Data Consortium, Philadelphia},
volume={4},
number={1},
pages={34},
year={2003}
}
@article{Rush_2015,
title={A Neural Attention Model for Abstractive Sentence Summarization},
url={http://dx.doi.org/10.18653/v1/D15-1044},
DOI={10.18653/v1/d15-1044},
journal={Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing},
publisher={Association for Computational Linguistics},
author={Rush, Alexander M. and Chopra, Sumit and Weston, Jason},
year={2015}
}
"""
_DESCRIPTION = """
Headline-generation on a corpus of article pairs from Gigaword consisting of
around 4 million articles. Use the 'org_data' provided by
https://github.com/microsoft/unilm/ which is identical to
https://github.com/harvardnlp/sent-summary but with better format.
There are two features:
- document: article.
- summary: headline.
"""
_URL = "https://drive.google.com/uc?export=download&id=1USoQ8lJgN8kAWnUnRrupMGrPMLlDVqlV"
_DOCUMENT = "document"
_SUMMARY = "summary"
class Gigaword(datasets.GeneratorBasedBuilder):
"""Gigaword summarization dataset."""
# 1.0.0 contains a bug that uses validation data as training data.
# 1.1.0 Update to the correct train, validation and test data.
# 1.2.0 Replace <unk> with <UNK> in train/val to be consistent with test.
VERSION = datasets.Version("1.2.0")
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features({_DOCUMENT: datasets.Value("string"), _SUMMARY: datasets.Value("string")}),
supervised_keys=(_DOCUMENT, _SUMMARY),
homepage="https://github.com/harvardnlp/sent-summary",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
dl_path = dl_manager.download_and_extract(_URL)
pattern = os.path.join(dl_path, "org_data", "%s.%s.txt")
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"src_path": pattern % ("train", "src"),
"tgt_path": pattern % ("train", "tgt"),
"replace_unk": True,
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"src_path": pattern % ("dev", "src"),
"tgt_path": pattern % ("dev", "tgt"),
"replace_unk": True,
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"src_path": pattern % ("test", "src"),
"tgt_path": pattern % ("test", "tgt"),
"replace_unk": False,
},
),
]
def _generate_examples(self, src_path=None, tgt_path=None, replace_unk=None):
"""Yields examples."""
with open(src_path, encoding="utf-8") as f_d, open(tgt_path, encoding="utf-8") as f_s:
for i, (doc_text, sum_text) in enumerate(zip(f_d, f_s)):
if replace_unk:
yield i, {
_DOCUMENT: doc_text.strip().replace("<unk>", "UNK"),
_SUMMARY: sum_text.strip().replace("<unk>", "UNK"),
}
else:
yield i, {_DOCUMENT: doc_text.strip(), _SUMMARY: sum_text.strip()}