Datasets:
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import json
import os
import datasets
from datasets.tasks import TextClassification
_DESCRIPTION = """
GovReport dataset for summarization.
From paper: Efficient Attentions for Long Document Summarization" by L. Huang et al.
See: https://arxiv.org/pdf/2104.02112.pdf
See: https://github.com/luyang-huang96/LongDocSum
"""
_CITATION = """\
@misc{huang2021efficient,
title={Efficient Attentions for Long Document Summarization},
author={Luyang Huang and Shuyang Cao and Nikolaus Parulian and Heng Ji and Lu Wang},
year={2021},
eprint={2104.02112},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
}
"""
_ABSTRACT = "summary"
_ARTICLE = "report"
class GovReportSummarizationConfig(datasets.BuilderConfig):
"""BuilderConfig for GovReportSummarization."""
def __init__(self, **kwargs):
"""BuilderConfig for GovReportSummarization.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(GovReportSummarizationConfig, self).__init__(**kwargs)
class GovReportSummarizationDataset(datasets.GeneratorBasedBuilder):
"""GovReportSummarization Dataset."""
_TRAIN_FILE = "train.zip"
_VAL_FILE = "valid.zip"
_TEST_FILE = "test.zip"
BUILDER_CONFIGS = [
GovReportSummarizationConfig(
name="document",
version=datasets.Version("1.0.0"),
description="GovReport dataset for summarization, document",
),
]
DEFAULT_CONFIG_NAME = "document"
def _info(self):
# Should return a datasets.DatasetInfo object
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
_ARTICLE: datasets.Value("string"),
_ABSTRACT: datasets.Value("string"),
#"id": datasets.Value("string"),
}
),
supervised_keys=None,
homepage="https://github.com/luyang-huang96/LongDocSum",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
train_path = os.path.join(dl_manager.download_and_extract(self._TRAIN_FILE), "train.txt")
val_path = os.path.join(dl_manager.download_and_extract(self._VAL_FILE), "valid.txt")
test_path = os.path.join(dl_manager.download_and_extract(self._TEST_FILE), "test.txt")
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path}
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION, gen_kwargs={"filepath": val_path}
),
datasets.SplitGenerator(
name=datasets.Split.TEST, gen_kwargs={"filepath": test_path}
),
]
def _generate_examples(self, filepath):
"""Generate GovReportSummarization examples."""
with open(filepath, encoding="utf-8") as f:
for id_, row in enumerate(f):
data = json.loads(row)
report = data["report"]
summary = data["summary"]
yield id_, {"report": report, "summary": summary}
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