|
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): |
|
|
|
return datasets.DatasetInfo( |
|
description=_DESCRIPTION, |
|
features=datasets.Features( |
|
{ |
|
_ARTICLE: datasets.Value("string"), |
|
_ABSTRACT: datasets.Value("string"), |
|
|
|
} |
|
), |
|
supervised_keys=None, |
|
homepage="https://github.com/luyang-huang96/LongDocSum", |
|
citation=_CITATION, |
|
) |
|
|
|
def _split_generators(self, dl_manager): |
|
|
|
train_path = dl_manager.download_and_extract(self._TRAIN_FILE) + "/train.txt" |
|
val_path = dl_manager.download_and_extract(self._VAL_FILE) + "/valid.txt" |
|
test_path = 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} |
|
|
