metadata
annotations_creators:
- no-annotation
language_creators:
- found
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
size_categories:
- 100K<n<1M
- 10K<n<100K
- 1M<n<10M
source_datasets:
- original
task_categories:
- summarization
task_ids: []
paperswithcode_id: bigpatent
pretty_name: Big Patent
configs:
- a
- all
- b
- c
- d
- e
- f
- g
- h
- 'y'
tags:
- patent-summarization
dataset_info:
- config_name: all
features:
- name: description
dtype: string
- name: abstract
dtype: string
splits:
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num_examples: 1207222
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num_examples: 67068
- name: test
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num_examples: 67072
download_size: 10142923776
dataset_size: 42612380671
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features:
- name: description
dtype: string
- name: abstract
dtype: string
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- name: test
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download_size: 10142923776
dataset_size: 6313418402
- config_name: b
features:
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dtype: string
- name: abstract
dtype: string
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download_size: 10142923776
dataset_size: 4702034848
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features:
- name: description
dtype: string
- name: abstract
dtype: string
splits:
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features:
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dtype: string
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- name: validation
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- name: test
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num_examples: 565
download_size: 10142923776
dataset_size: 293681324
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features:
- name: description
dtype: string
- name: abstract
dtype: string
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- name: validation
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num_examples: 1914
download_size: 10142923776
dataset_size: 978334020
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features:
- name: description
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- name: abstract
dtype: string
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- name: validation
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download_size: 10142923776
dataset_size: 2385612407
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features:
- name: description
dtype: string
- name: abstract
dtype: string
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dtype: string
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dtype: string
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download_size: 10142923776
dataset_size: 8968684827
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features:
- name: description
dtype: string
- name: abstract
dtype: string
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- name: validation
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- name: test
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num_examples: 6911
download_size: 10142923776
dataset_size: 4100353733
Dataset Card for Big Patent
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
- Homepage: Big Patent
- Repository:
- Paper: BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization
- Leaderboard:
- Point of Contact: Lu Wang
Dataset Summary
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
- y: General tagging of new or cross-sectional technology
Current defaults are 2.1.2 version (fix update to cased raw strings) and 'all' CPC codes:
from datasets import load_dataset
ds = load_dataset("big_patent") # default is 'all' CPC codes
ds = load_dataset("big_patent", "all") # the same as above
ds = load_dataset("big_patent", "a") # only 'a' CPC codes
ds = load_dataset("big_patent", codes=["a", "b"])
To use 1.0.0 version (lower cased tokenized words), pass both parameters codes and version:
ds = load_dataset("big_patent", codes="all", version="1.0.0")
ds = load_dataset("big_patent", codes="a", version="1.0.0")
ds = load_dataset("big_patent", codes=["a", "b"], version="1.0.0")
Supported Tasks and Leaderboards
[More Information Needed]
Languages
English
Dataset Structure
Data Instances
Each instance contains a pair of description and abstract. description is extracted from the Description section of the Patent while abstract is extracted from the Abstract section.
{
'description': 'FIELD OF THE INVENTION \n [0001] This invention relates to novel calcium phosphate-coated implantable medical devices and processes of making same. The unique calcium-phosphate coated implantable medical devices minimize...',
'abstract': 'This invention relates to novel calcium phosphate-coated implantable medical devices...'
}
Data Fields
description: detailed description of patent.abstract: Patent abastract.
Data Splits
| train | validation | test | |
|---|---|---|---|
| all | 1207222 | 67068 | 67072 |
| a | 174134 | 9674 | 9675 |
| b | 161520 | 8973 | 8974 |
| c | 101042 | 5613 | 5614 |
| d | 10164 | 565 | 565 |
| e | 34443 | 1914 | 1914 |
| f | 85568 | 4754 | 4754 |
| g | 258935 | 14385 | 14386 |
| h | 257019 | 14279 | 14279 |
| y | 124397 | 6911 | 6911 |
Dataset Creation
Curation Rationale
[More Information Needed]
Source Data
Initial Data Collection and Normalization
[More Information Needed]
Who are the source language producers?
[More Information Needed]
Annotations
Annotation process
[More Information Needed]
Who are the annotators?
[More Information Needed]
Personal and Sensitive Information
[More Information Needed]
Considerations for Using the Data
Social Impact of Dataset
[More Information Needed]
Discussion of Biases
[More Information Needed]
Other Known Limitations
[More Information Needed]
Additional Information
Dataset Curators
[More Information Needed]
Licensing Information
[More Information Needed]
Citation Information
@article{DBLP:journals/corr/abs-1906-03741,
author = {Eva Sharma and
Chen Li and
Lu Wang},
title = {{BIGPATENT:} {A} Large-Scale Dataset for Abstractive and Coherent
Summarization},
journal = {CoRR},
volume = {abs/1906.03741},
year = {2019},
url = {http://arxiv.org/abs/1906.03741},
eprinttype = {arXiv},
eprint = {1906.03741},
timestamp = {Wed, 26 Jun 2019 07:14:58 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1906-03741.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Contributions
Thanks to @mattbui for adding this dataset.