Datasets:
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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
"""MediaSum dataset"""
import os
import json
import datasets
logger = datasets.logging.get_logger(__name__)
_HOMEPAGE = "https://github.com/zcgzcgzcg1/MediaSum"
_DESCRIPTION = """\
This large-scale media interview dataset contains 463.6K transcripts with abstractive summaries,
collected from interview transcripts and overview / topic descriptions from NPR and CNN.
"""
_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}
}
"""
_DOWNLOAD_URLS = {
"train": "https://huggingface.co/datasets/nbroad/mediasum/resolve/main/train.json",
"validation": "https://huggingface.co/datasets/nbroad/mediasum/resolve/main/validation.json",
"test": "https://huggingface.co/datasets/nbroad/mediasum/resolve/main/test.json",
}
class MediaSumConfig(datasets.BuilderConfig):
"""BuilderConfig for MediaSum."""
def __init__(self, **kwargs):
"""BuilderConfig for MediaSum.
Args:
**kwargs: keyword arguments forwarded to super.
"""
super().__init__(**kwargs)
class MediaSum(datasets.GeneratorBasedBuilder):
"""MediaSum summarization dataset."""
BUILDER_CONFIGS = [MediaSumConfig(name="mediasum", description="Plain text")]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"id": datasets.Value("string"),
"program": datasets.Value("string"),
"date": datasets.Value("string"),
"url": datasets.Value("string"),
"title": datasets.Value("string"),
"summary": datasets.Value("string"),
"utt": datasets.features.Sequence(
datasets.Value("string")
),
"speaker": datasets.features.Sequence(
datasets.Value("string")
),
}
),
supervised_keys=None,
homepage=_HOMEPAGE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
dl_path = dl_manager.download(_DOWNLOAD_URLS)
return [
datasets.SplitGenerator(
name=split,
gen_kwargs={
"filepath": dl_path[split],
},
)
for split in [
datasets.Split.TRAIN,
datasets.Split.VALIDATION,
datasets.Split.TEST,
]
]
def _generate_examples(self, filepath):
with open(filepath, "r") as fp:
for idx, line in enumerate(fp):
data = json.loads(line)
# Some do not have titles
if "title" not in data:
data["title"] = ""
yield idx, data
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