Datasets:

FiscalNote
/

billsum

	# 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"]}