
# 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.
task_categories:summarization annotations_creators:no-annotation language_creators:found multilinguality:monolingual size_categories:100K<n<1M size_categories:10K<n<100K size_categories:1M<n<10M source_datasets:original language:en license:cc-by-4.0 patent-summarization arxiv:1906.03741 region:us
{"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", "config_names": ["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}]}
TAGS
#task_categories-summarization #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-100K<n<1M #size_categories-10K<n<100K #size_categories-1M<n<10M #source_datasets-original #language-English #license-cc-by-4.0 #patent-summarization #arxiv-1906.03741 #region-us 

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
	+ Annotations
	+ 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


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:


To use 1.0.0 version (lower cased tokenized words), pass both parameters 'codes' and 'version':


### Supported Tasks and Leaderboards


### 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



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


### Contributions


Thanks to @mattbui for adding this dataset.



### 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:


To use 1.0.0 version (lower cased tokenized words), pass both parameters 'codes' and 'version': ### Supported Tasks and Leaderboards ### 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



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 ### Contributions


Thanks to @mattbui for adding this dataset.
TAGS
#task_categories-summarization #annotations_creators-no-annotation #language_creators-found #multilinguality-monolingual #size_categories-100K<n<1M #size_categories-10K<n<100K #size_categories-1M<n<10M #source_datasets-original #language-English #license-cc-by-4.0 #patent-summarization #arxiv-1906.03741 #region-us 
 ### 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:


To use 1.0.0 version (lower cased tokenized words), pass both parameters 'codes' and 'version': ### Supported Tasks and Leaderboards ### 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



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 ### Contributions


Thanks to @mattbui for adding this dataset.