Datasets:
Tasks:
Summarization
Languages:
English
Multilinguality:
monolingual
Language Creators:
found
Annotations Creators:
no-annotation
Source Datasets:
original
ArXiv:
Tags:
patent-summarization
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 | |
"""BigPatent Dataset.""" | |
import gzip | |
import json | |
import os | |
import datasets | |
_HOMEPAGE = "https://evasharma.github.io/bigpatent/" | |
_CITATION = """ | |
@misc{sharma2019bigpatent, | |
title={BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization}, | |
author={Eva Sharma and Chen Li and Lu Wang}, | |
year={2019}, | |
eprint={1906.03741}, | |
archivePrefix={arXiv}, | |
primaryClass={cs.CL} | |
} | |
""" | |
_DESCRIPTION = """ | |
BIGPATENT, consisting of 1.3 million records of U.S. patent documents | |
along with human written abstractive summaries. | |
Each US patent application is filed under a Cooperative Patent Classification | |
(CPC) code. There are nine such classification categories: | |
A (Human Necessities), B (Performing Operations; Transporting), | |
C (Chemistry; Metallurgy), D (Textiles; Paper), E (Fixed Constructions), | |
F (Mechanical Engineering; Lightning; Heating; Weapons; Blasting), | |
G (Physics), H (Electricity), and | |
Y (General tagging of new or cross-sectional technology) | |
There are two features: | |
- description: detailed description of patent. | |
- abstract: Patent abastract. | |
""" | |
_LICENSE = "Creative Commons Attribution 4.0 International" | |
_SPLIT_NAMES = {datasets.Split.TRAIN: "train", datasets.Split.VALIDATION: "val", datasets.Split.TEST: "test"} | |
_URL = "data/{version}/{split_name}.zip" | |
_DOCUMENT = "description" | |
_SUMMARY = "abstract" | |
_CPC_DESCRIPTION = { | |
"a": "Human Necessities", | |
"b": "Performing Operations; Transporting", | |
"c": "Chemistry; Metallurgy", | |
"d": "Textiles; Paper", | |
"e": "Fixed Constructions", | |
"f": "Mechanical Engineering; Lightning; Heating; Weapons; Blasting", | |
"g": "Physics", | |
"h": "Electricity", | |
"y": "General tagging of new or cross-sectional technology", | |
} | |
# Available versions: | |
# 1.0.0 lower cased tokenized words. | |
# 2.0.0 cased raw strings. | |
# 2.1.2 cased raw strings (fixed). | |
_VERSION = "2.1.2" | |
class BigPatentConfig(datasets.BuilderConfig): | |
"""BuilderConfig for BigPatent.""" | |
def __init__(self, codes="all", version=_VERSION, **kwargs): | |
"""BuilderConfig for BigPatent. | |
Args: | |
codes (str or list, default 'all'): CPC codes. Either 'all' or a combination | |
of {'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'y'}. | |
**kwargs: keyword arguments forwarded to super. | |
""" | |
if isinstance(codes, str): | |
codes = [codes] | |
name = "+".join(codes) | |
if name == "all": | |
codes = list(_CPC_DESCRIPTION) | |
if version != _VERSION: | |
name = f"{name}-{version}" | |
super().__init__(name=name, version=version, **kwargs) | |
self.codes = codes | |
class BigPatent(datasets.GeneratorBasedBuilder): | |
"""BigPatent datasets.""" | |
BUILDER_CONFIG_CLASS = BigPatentConfig | |
BUILDER_CONFIGS = [ | |
BigPatentConfig( | |
codes="all", | |
description="Patents under all categories.", | |
), | |
] + [ | |
BigPatentConfig( | |
codes=k, | |
description=f"Patents under Cooperative Patent Classification (CPC) {k}: {v}", | |
) | |
for k, v in sorted(_CPC_DESCRIPTION.items()) | |
] | |
DEFAULT_CONFIG_NAME = "all" | |
VERSION = _VERSION | |
def _info(self): | |
return datasets.DatasetInfo( | |
description=_DESCRIPTION, | |
features=datasets.Features({_DOCUMENT: datasets.Value("string"), _SUMMARY: datasets.Value("string")}), | |
supervised_keys=(_DOCUMENT, _SUMMARY), | |
homepage=_HOMEPAGE, | |
license=_LICENSE, | |
citation=_CITATION, | |
) | |
def _split_generators(self, dl_manager): | |
"""Returns SplitGenerators.""" | |
urls = { | |
split: _URL.format(version=self.config.version, split_name=split_name) | |
for split, split_name in _SPLIT_NAMES.items() | |
} | |
dl_paths = dl_manager.download_and_extract(urls) | |
paths = { | |
split: [ | |
dl_manager.iter_files(os.path.join(dl_paths[split], split_name, code)) for code in self.config.codes | |
] | |
for split, split_name in _SPLIT_NAMES.items() | |
} | |
return [ | |
datasets.SplitGenerator( | |
name=split, | |
gen_kwargs={"paths": paths[split]}, | |
) | |
for split in _SPLIT_NAMES | |
] | |
def _generate_examples(self, paths=None): | |
"""Yields examples.""" | |
for paths_per_code in paths: | |
for path in paths_per_code: | |
with open(path, "rb") as fin: | |
fin = gzip.GzipFile(fileobj=fin) | |
for row in fin: | |
json_obj = json.loads(row) | |
yield json_obj["publication_number"], { | |
_DOCUMENT: json_obj[_DOCUMENT], | |
_SUMMARY: json_obj[_SUMMARY], | |
} | |