|
--- |
|
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: |
|
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|
num_bytes: 38367048389 |
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num_examples: 1207222 |
|
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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 |
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splits: |
|
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|
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num_examples: 9674 |
|
- name: test |
|
num_bytes: 316633277 |
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num_examples: 9675 |
|
download_size: 10142923776 |
|
dataset_size: 6313418402 |
|
- config_name: b |
|
features: |
|
- name: description |
|
dtype: string |
|
- name: abstract |
|
dtype: string |
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splits: |
|
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num_examples: 8973 |
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- name: test |
|
num_bytes: 231538734 |
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num_examples: 8974 |
|
download_size: 10142923776 |
|
dataset_size: 4702034848 |
|
- config_name: c |
|
features: |
|
- name: description |
|
dtype: string |
|
- name: abstract |
|
dtype: string |
|
splits: |
|
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num_examples: 101042 |
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num_examples: 5613 |
|
- name: test |
|
num_bytes: 252566793 |
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num_examples: 5614 |
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download_size: 10142923776 |
|
dataset_size: 5003500874 |
|
- config_name: d |
|
features: |
|
- name: description |
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dtype: string |
|
- name: abstract |
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dtype: string |
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splits: |
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num_examples: 10164 |
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num_examples: 565 |
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- name: test |
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num_bytes: 14403430 |
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num_examples: 565 |
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download_size: 10142923776 |
|
dataset_size: 293681324 |
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- config_name: e |
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features: |
|
- name: description |
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dtype: string |
|
- name: abstract |
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dtype: string |
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splits: |
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num_bytes: 881101433 |
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num_examples: 34443 |
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num_examples: 1914 |
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- name: test |
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num_bytes: 48586429 |
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num_examples: 1914 |
|
download_size: 10142923776 |
|
dataset_size: 978334020 |
|
- config_name: f |
|
features: |
|
- name: description |
|
dtype: string |
|
- name: abstract |
|
dtype: string |
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splits: |
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num_examples: 4754 |
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download_size: 10142923776 |
|
dataset_size: 2385612407 |
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- config_name: g |
|
features: |
|
- name: description |
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dtype: string |
|
- name: abstract |
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splits: |
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download_size: 10142923776 |
|
dataset_size: 9866760236 |
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- config_name: h |
|
features: |
|
- name: description |
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dtype: string |
|
- name: abstract |
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dtype: string |
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splits: |
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num_examples: 14279 |
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download_size: 10142923776 |
|
dataset_size: 8968684827 |
|
- config_name: y |
|
features: |
|
- name: description |
|
dtype: string |
|
- name: abstract |
|
dtype: string |
|
splits: |
|
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num_examples: 6911 |
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num_examples: 6911 |
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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. |
