README.md

---
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:
  - name: train
    num_bytes: 38367048389
    num_examples: 1207222
  - name: validation
    num_bytes: 2115827002
    num_examples: 67068
  - name: test
    num_bytes: 2129505280
    num_examples: 67072
  download_size: 10142923776
  dataset_size: 42612380671
- config_name: a
  features:
  - name: description
    dtype: string
  - name: abstract
    dtype: string
  splits:
  - name: train
    num_bytes: 5683460620
    num_examples: 174134
  - name: validation
    num_bytes: 313324505
    num_examples: 9674
  - name: test
    num_bytes: 316633277
    num_examples: 9675
  download_size: 10142923776
  dataset_size: 6313418402
- config_name: b
  features:
  - name: description
    dtype: string
  - name: abstract
    dtype: string
  splits:
  - name: train
    num_bytes: 4236070976
    num_examples: 161520
  - name: validation
    num_bytes: 234425138
    num_examples: 8973
  - name: test
    num_bytes: 231538734
    num_examples: 8974
  download_size: 10142923776
  dataset_size: 4702034848
- config_name: c
  features:
  - name: description
    dtype: string
  - name: abstract
    dtype: string
  splits:
  - name: train
    num_bytes: 4506249306
    num_examples: 101042
  - name: validation
    num_bytes: 244684775
    num_examples: 5613
  - name: test
    num_bytes: 252566793
    num_examples: 5614
  download_size: 10142923776
  dataset_size: 5003500874
- config_name: d
  features:
  - name: description
    dtype: string
  - name: abstract
    dtype: string
  splits:
  - name: train
    num_bytes: 264717412
    num_examples: 10164
  - name: validation
    num_bytes: 14560482
    num_examples: 565
  - name: test
    num_bytes: 14403430
    num_examples: 565
  download_size: 10142923776
  dataset_size: 293681324
- config_name: e
  features:
  - name: description
    dtype: string
  - name: abstract
    dtype: string
  splits:
  - name: train
    num_bytes: 881101433
    num_examples: 34443
  - name: validation
    num_bytes: 48646158
    num_examples: 1914
  - name: test
    num_bytes: 48586429
    num_examples: 1914
  download_size: 10142923776
  dataset_size: 978334020
- config_name: f
  features:
  - name: description
    dtype: string
  - name: abstract
    dtype: string
  splits:
  - name: train
    num_bytes: 2146383473
    num_examples: 85568
  - name: validation
    num_bytes: 119632631
    num_examples: 4754
  - name: test
    num_bytes: 119596303
    num_examples: 4754
  download_size: 10142923776
  dataset_size: 2385612407
- config_name: g
  features:
  - name: description
    dtype: string
  - name: abstract
    dtype: string
  splits:
  - name: train
    num_bytes: 8877854206
    num_examples: 258935
  - name: validation
    num_bytes: 492581177
    num_examples: 14385
  - name: test
    num_bytes: 496324853
    num_examples: 14386
  download_size: 10142923776
  dataset_size: 9866760236
- config_name: h
  features:
  - name: description
    dtype: string
  - name: abstract
    dtype: string
  splits:
  - name: train
    num_bytes: 8075621958
    num_examples: 257019
  - name: validation
    num_bytes: 447602356
    num_examples: 14279
  - name: test
    num_bytes: 445460513
    num_examples: 14279
  download_size: 10142923776
  dataset_size: 8968684827
- config_name: y
  features:
  - name: description
    dtype: string
  - name: abstract
    dtype: string
  splits:
  - name: train
    num_bytes: 3695589005
    num_examples: 124397
  - name: validation
    num_bytes: 200369780
    num_examples: 6911
  - name: test
    num_bytes: 204394948
    num_examples: 6911
  download_size: 10142923776
  dataset_size: 4100353733
---

# Dataset Card for Big Patent

## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)

## Dataset Description

- **Homepage:** [Big Patent](https://evasharma.github.io/bigpatent/)
- **Repository:**
- **Paper:** [BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization](https://arxiv.org/abs/1906.03741)
- **Leaderboard:**
- **Point of Contact:** [Lu Wang](mailto:wangluxy@umich.edu)

### 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:
```python
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`:
```python
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

```bibtex
@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](https://github.com/mattbui) for adding this dataset.