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Datasets:
big_patent

Tasks:
Summarization
Languages:
English
Multilinguality:
monolingual
Size Categories:
100K<n<1M
10K<n<100K
1M<n<10M
Language Creators:
found
Annotations Creators:
no-annotation
Source Datasets:
original
ArXiv:
Tags:
patent-summarization
License:
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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:
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        dtype: string
      - name: abstract
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Dataset Card for Big Patent

Table of Contents

Dataset Description

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.