Datasets:
Tasks:
Summarization
Modalities:
Text
Formats:
csv
Languages:
English
Size:
10K - 100K
ArXiv:
Tags:
patent-summarization
License:
Commit
·
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Parent(s):
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Upload README.md with huggingface_hub
Browse files
README.md
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- found
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language:
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- en
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license:
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- cc-by-4.0
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multilinguality:
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source_datasets:
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task_categories:
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- summarization
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task_ids: []
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paperswithcode_id: bigpatent
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pretty_name: Big Patent
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- a
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- all
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---
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# Sampled big_patent Dataset
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This is a sampled big_patent dataset containing 5000 train and 500 test rows.
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The original repo card follows below.
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# Dataset Card for Big Patent
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## Table of Contents
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- [Dataset Description](#dataset-description)
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- [Dataset Summary](#dataset-summary)
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- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
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- [Languages](#languages)
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- [Dataset Structure](#dataset-structure)
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- [Data Instances](#data-instances)
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- [Data Fields](#data-fields)
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- [Data Splits](#data-splits)
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- [Dataset Creation](#dataset-creation)
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- [Curation Rationale](#curation-rationale)
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- [Source Data](#source-data)
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- [Annotations](#annotations)
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- [Personal and Sensitive Information](#personal-and-sensitive-information)
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- [Considerations for Using the Data](#considerations-for-using-the-data)
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- [Social Impact of Dataset](#social-impact-of-dataset)
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- [Discussion of Biases](#discussion-of-biases)
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- [Other Known Limitations](#other-known-limitations)
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- [Additional Information](#additional-information)
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- [Dataset Curators](#dataset-curators)
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- [Licensing Information](#licensing-information)
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- [Citation Information](#citation-information)
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- [Contributions](#contributions)
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-
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## Dataset Description
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- **Homepage:** [Big Patent](https://evasharma.github.io/bigpatent/)
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- **Repository:**
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- **Paper:** [BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization](https://arxiv.org/abs/1906.03741)
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- **Leaderboard:**
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- **Point of Contact:** [Lu Wang](mailto:wangluxy@umich.edu)
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### Dataset Summary
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BIGPATENT, consisting of 1.3 million records of U.S. patent documents along with human written abstractive summaries.
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Each US patent application is filed under a Cooperative Patent Classification (CPC) code.
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There are nine such classification categories:
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- a: Human Necessities
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- b: Performing Operations; Transporting
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- c: Chemistry; Metallurgy
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- d: Textiles; Paper
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- e: Fixed Constructions
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- f: Mechanical Engineering; Lightning; Heating; Weapons; Blasting
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- g: Physics
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- h: Electricity
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- y: General tagging of new or cross-sectional technology
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Current defaults are 2.1.2 version (fix update to cased raw strings) and 'all' CPC codes:
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```python
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from datasets import load_dataset
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ds = load_dataset("big_patent") # default is 'all' CPC codes
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ds = load_dataset("big_patent", "all") # the same as above
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ds = load_dataset("big_patent", "a") # only 'a' CPC codes
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ds = load_dataset("big_patent", codes=["a", "b"])
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```
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To use 1.0.0 version (lower cased tokenized words), pass both parameters `codes` and `version`:
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```python
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ds = load_dataset("big_patent", codes="all", version="1.0.0")
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ds = load_dataset("big_patent", codes="a", version="1.0.0")
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ds = load_dataset("big_patent", codes=["a", "b"], version="1.0.0")
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```
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### Supported Tasks and Leaderboards
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-
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[More Information Needed]
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-
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### Languages
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-
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English
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## Dataset Structure
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### Data Instances
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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.
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```
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{
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'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...',
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'abstract': 'This invention relates to novel calcium phosphate-coated implantable medical devices...'
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}
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```
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### Data Fields
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- `description`: detailed description of patent.
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- `abstract`: Patent abastract.
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### Data Splits
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| | train | validation | test |
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|:----|------------------:|-------------:|-------:|
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| all | 1207222 | 67068 | 67072 |
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| a | 174134 | 9674 | 9675 |
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| b | 161520 | 8973 | 8974 |
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| c | 101042 | 5613 | 5614 |
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| d | 10164 | 565 | 565 |
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| e | 34443 | 1914 | 1914 |
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| f | 85568 | 4754 | 4754 |
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| g | 258935 | 14385 | 14386 |
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| h | 257019 | 14279 | 14279 |
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| y | 124397 | 6911 | 6911 |
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-
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## Dataset Creation
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| 330 |
-
|
| 331 |
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### Curation Rationale
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| 332 |
-
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[More Information Needed]
|
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-
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### Source Data
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-
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#### Initial Data Collection and Normalization
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-
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[More Information Needed]
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-
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#### Who are the source language producers?
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-
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[More Information Needed]
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-
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### Annotations
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-
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#### Annotation process
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| 348 |
-
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| 349 |
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[More Information Needed]
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| 350 |
-
|
| 351 |
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#### Who are the annotators?
|
| 352 |
-
|
| 353 |
-
[More Information Needed]
|
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-
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| 355 |
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### Personal and Sensitive Information
|
| 356 |
-
|
| 357 |
-
[More Information Needed]
|
| 358 |
-
|
| 359 |
-
## Considerations for Using the Data
|
| 360 |
-
|
| 361 |
-
### Social Impact of Dataset
|
| 362 |
-
|
| 363 |
-
[More Information Needed]
|
| 364 |
-
|
| 365 |
-
### Discussion of Biases
|
| 366 |
-
|
| 367 |
-
[More Information Needed]
|
| 368 |
-
|
| 369 |
-
### Other Known Limitations
|
| 370 |
-
|
| 371 |
-
[More Information Needed]
|
| 372 |
-
|
| 373 |
-
## Additional Information
|
| 374 |
-
|
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### Dataset Curators
|
| 376 |
-
|
| 377 |
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[More Information Needed]
|
| 378 |
-
|
| 379 |
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### Licensing Information
|
| 380 |
-
|
| 381 |
-
[More Information Needed]
|
| 382 |
-
|
| 383 |
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### Citation Information
|
| 384 |
-
|
| 385 |
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```bibtex
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@article{DBLP:journals/corr/abs-1906-03741,
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author = {Eva Sharma and
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Chen Li and
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Lu Wang},
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title = {{BIGPATENT:} {A} Large-Scale Dataset for Abstractive and Coherent
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Summarization},
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journal = {CoRR},
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volume = {abs/1906.03741},
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year = {2019},
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url = {http://arxiv.org/abs/1906.03741},
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eprinttype = {arXiv},
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eprint = {1906.03741},
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timestamp = {Wed, 26 Jun 2019 07:14:58 +0200},
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biburl = {https://dblp.org/rec/journals/corr/abs-1906-03741.bib},
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bibsource = {dblp computer science bibliography, https://dblp.org}
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}
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-
```
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-
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### Contributions
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Thanks to [@mattbui](https://github.com/mattbui) for adding this dataset.
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| 1 |
# Sampled big_patent Dataset
|
| 2 |
+
This is a sampled big_patent dataset containing 5000 train and 500 test rows.
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