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import json
import os
import datasets
from datasets.tasks import TextClassification
_CITATION = None
_DESCRIPTION = """
MediaSum dataset for summarization.
From paper: "MediaSum: A Large-scale Media Interview Dataset for Dialogue Summarization" by C. Zhu et al."
"""
_CITATION = """\
@article{zhu2021mediasum,
title={MediaSum: A Large-scale Media Interview Dataset for Dialogue Summarization},
author={Zhu, Chenguang and Liu, Yang and Mei, Jie and Zeng, Michael},
journal={arXiv preprint arXiv:2103.06410},
year={2021}
}
"""
_ABSTRACT = "summary"
_ARTICLE = "document"
class MediaSumSummarizationConfig(datasets.BuilderConfig):
"""BuilderConfig for MediaSumSummarization."""
def __init__(self, **kwargs):
"""BuilderConfig for MediaSumSummarization.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super(MediaSumSummarizationConfig, self).__init__(**kwargs)
class MediaSumSummarizationDataset(datasets.GeneratorBasedBuilder):
"""MediaSumSummarization Dataset."""
_TRAIN_FILE = "train_data.zip"
_VAL_FILE = "val_data.zip"
_TEST_FILE = "test_data.zip"
BUILDER_CONFIGS = [
MediaSumSummarizationConfig(
name="newline",
version=datasets.Version("1.0.0"),
description="MediaSum dataset for summarization, concat sections",
),
MediaSumSummarizationConfig(
name="roberta",
version=datasets.Version("1.0.0"),
description="MediaSum dataset for summarization, document",
),
MediaSumSummarizationConfig(
name="bert",
version=datasets.Version("1.0.0"),
description="MediaSum dataset for summarization, document",
),
MediaSumSummarizationConfig(
name="list",
version=datasets.Version("1.0.0"),
description="MediaSum dataset for summarization, document",
),
]
DEFAULT_CONFIG_NAME = "roberta"
def _info(self):
# Should return a datasets.DatasetInfo object
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
_ARTICLE: datasets.Sequence(datasets.Value("string")) if self.config.name == "list" else datasets.Value("string"),
_ABSTRACT: datasets.Value("string"),
#"id": datasets.Value("string"),
}
),
supervised_keys=None,
homepage="https://github.com/zcgzcgzcg1/MediaSum",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
train_path = dl_manager.download_and_extract(self._TRAIN_FILE) + "/train_data.txt"
val_path = dl_manager.download_and_extract(self._VAL_FILE) + "/val_data.txt"
test_path = dl_manager.download_and_extract(self._TEST_FILE) + "/test_data.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 MediaSumSummarization examples."""
if self.config.name == "newline":
join_ = "\n"
elif self.config.name == "roberta":
join_ = "</s>"
elif self.config.name == "bert":
join_ = "[SEP]"
with open(filepath, encoding="utf-8") as f:
for id_, row in enumerate(f):
data = json.loads(row)
"""
'summary': str,
'document': List[str],
"""
document = data["utt"]
if self.config.name != "list":
document = join_.join(document)
summary = data["summary"]
yield id_, {"document": document, "summary": summary}
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