Datasets:
Tasks:
Summarization
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
10K<n<100K
ArXiv:
License:
File size: 3,663 Bytes
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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
"""BillSum Dataset."""
import json
import os
import datasets
_CITATION = """
@misc{kornilova2019billsum,
title={BillSum: A Corpus for Automatic Summarization of US Legislation},
author={Anastassia Kornilova and Vlad Eidelman},
year={2019},
eprint={1910.00523},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
"""
_DESCRIPTION = """
BillSum, summarization of US Congressional and California state bills.
There are several features:
- text: bill text.
- summary: summary of the bills.
- title: title of the bills.
features for us bills. ca bills does not have.
- text_len: number of chars in text.
- sum_len: number of chars in summary.
"""
_URL = "https://drive.google.com/uc?export=download&id=1g89WgFHMRbr4QrvA0ngh26PY081Nv3lx"
_LICENSE = "CC0"
_DOCUMENT = "text"
_SUMMARY = "summary"
class Billsum(datasets.GeneratorBasedBuilder):
"""BillSum Dataset."""
# 2.0.0 data source updated to filter near duplicates.
# 3.0.0 none of the test examples are 'near duplicates' of an example in the
# train set AND they dont have the same title, regardless of similarity.
VERSION = datasets.Version("3.0.0")
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
license=_LICENSE,
features=datasets.Features(
{
_DOCUMENT: datasets.Value("string"),
_SUMMARY: datasets.Value("string"),
"title": datasets.Value("string"),
}
),
supervised_keys=(_DOCUMENT, _SUMMARY),
homepage="https://github.com/FiscalNote/BillSum",
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
dl_path = dl_manager.download_and_extract(_URL)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"path": os.path.join(dl_path, "us_train_data_final_OFFICIAL.jsonl"), "key": "bill_id"},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"path": os.path.join(dl_path, "us_test_data_final_OFFICIAL.jsonl"), "key": "bill_id"},
),
datasets.SplitGenerator(
name="ca_test",
gen_kwargs={"path": os.path.join(dl_path, "ca_test_data_final_OFFICIAL.jsonl"), "key": "external_id"},
),
]
def _generate_examples(self, path=None, key=None):
"""Yields examples."""
with open(path, encoding="utf-8") as f:
for line in f:
# in us bills, json has fields:
# text, summary, title, bill_id, text_len, sum_len
# in ca bills, json has fields:
# text, summary, title, external_id
d = json.loads(line)
yield d[key], {k: d[k] for k in [_DOCUMENT, _SUMMARY, "title"]}
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