README.md

---
annotations_creators:
- no-annotation
language_creators:
- found
languages:
- en
licenses:
- unknown
multilinguality:
- monolingual
size_categories:
  a:
  - 100K<n<1M
  all:
  - 1M<n<10M
  b:
  - 100K<n<1M
  c:
  - 100K<n<1M
  d:
  - 10K<n<100K
  e:
  - 10K<n<100K
  f:
  - 10K<n<100K
  g:
  - 100K<n<1M
  h:
  - 100K<n<1M
  y:
  - 100K<n<1M
source_datasets:
- original
task_categories:
- conditional-text-generation
task_ids:
- summarization
paperswithcode_id: bigpatent
pretty_name: Big Patent
---

# 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:**

### 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)

### 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.

### 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.