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
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
      - name: validation
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        num_examples: 67068
      - name: test
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        num_examples: 67072
    download_size: 10142923776
    dataset_size: 42612380671
  - config_name: a
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        dtype: string
      - name: abstract
        dtype: string
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      - name: validation
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        num_examples: 9674
      - name: test
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        num_examples: 9675
    download_size: 10142923776
    dataset_size: 6313418402
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      - name: abstract
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      - name: validation
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      - name: test
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    download_size: 10142923776
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      - name: abstract
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      - name: abstract
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      - name: validation
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      - name: test
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    download_size: 10142923776
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      - name: abstract
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      - name: validation
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      - name: test
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    download_size: 10142923776
    dataset_size: 978334020
  - config_name: f
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      - name: abstract
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      - name: validation
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      - name: test
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  - config_name: g
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      - name: abstract
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      - name: validation
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      - name: test
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      - name: description
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      - name: abstract
        dtype: string
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      - name: validation
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      - name: test
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        num_examples: 14279
    download_size: 10142923776
    dataset_size: 8968684827
  - config_name: 'y'
    features:
      - name: description
        dtype: string
      - name: abstract
        dtype: string
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      - name: train
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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
config_names:
  - a
  - all
  - b
  - c
  - d
  - e
  - f
  - g
  - h
  - 'y'

Sampled big_patent Dataset

This is a sampled big_patent dataset containing 5000 train and 500 test rows.

The original repo card follows below.

Dataset Card for Big Patent

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.

Datasets:

Trelis
/

big_patent_sample

Sampled big_patent Dataset

Dataset Card for Big Patent

Table of Contents

Dataset Description

Dataset Summary

Supported Tasks and Leaderboards

Languages

Dataset Structure

Data Instances

Data Fields

Data Splits

Dataset Creation

Curation Rationale

Source Data

Initial Data Collection and Normalization

Who are the source language producers?

Annotations

Annotation process

Who are the annotators?

Personal and Sensitive Information

Considerations for Using the Data

Social Impact of Dataset

Discussion of Biases

Other Known Limitations

Additional Information

Dataset Curators

Licensing Information

Citation Information

Contributions