Datasets:
Tasks:
Summarization
Languages:
English
Multilinguality:
monolingual
Size Categories:
100K<n<1M
ArXiv:
Tags:
License:
| # 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 | |