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## Dataset Summary
A dataset for benchmarking keyphrase extraction and generation techniques from abstracts of english scientific papers. For more details about the dataset please refer the original paper - [https://aclanthology.org/D14-1150.pdf](https://aclanthology.org/D14-1150.pdf)
Original source of the data - []()
## Dataset Structure
### Data Fields
- **id**: unique identifier of the document.
- **document**: Whitespace separated list of words in the document.
- **doc_bio_tags**: BIO tags for each word in the document. B stands for the beginning of a keyphrase and I stands for inside the keyphrase. O stands for outside the keyphrase and represents the word that isn't a part of the keyphrase at all.
- **extractive_keyphrases**: List of all the present keyphrases.
- **abstractive_keyphrase**: List of all the absent keyphrases.
### Data Splits
|Split| #datapoints |
|--|--|
| Test | 755 |
- Percentage of keyphrases that are named entities: 56.99% (named entities detected using scispacy - en-core-sci-lg model)
- Percentage of keyphrases that are noun phrases: 54.99% (noun phrases detected using spacy en-core-web-lg after removing determiners)
## Usage
### Full Dataset
```python
from datasets import load_dataset
# get entire dataset
dataset = load_dataset("midas/kdd", "raw")
# sample from the test split
print("Sample from test dataset split")
test_sample = dataset["test"][0]
print("Fields in the sample: ", [key for key in test_sample.keys()])
print("Tokenized Document: ", test_sample["document"])
print("Document BIO Tags: ", test_sample["doc_bio_tags"])
print("Extractive/present Keyphrases: ", test_sample["extractive_keyphrases"])
print("Abstractive/absent Keyphrases: ", test_sample["abstractive_keyphrases"])
print("\n-----------\n")
```
**Output**
```bash
Sample from test data split
Fields in the sample: ['id', 'document', 'doc_bio_tags', 'extractive_keyphrases', 'abstractive_keyphrases', 'other_metadata']
Tokenized Document: ['Discovering', 'roll-up', 'dependencies']
Document BIO Tags: ['O', 'O', 'O']
Extractive/present Keyphrases: []
Abstractive/absent Keyphrases: ['logical design']
-----------
```
### Keyphrase Extraction
```python
from datasets import load_dataset
# get the dataset only for keyphrase extraction
dataset = load_dataset("midas/kdd", "extraction")
print("Samples for Keyphrase Extraction")
# sample from the test split
print("Sample from test data split")
test_sample = dataset["test"][0]
print("Fields in the sample: ", [key for key in test_sample.keys()])
print("Tokenized Document: ", test_sample["document"])
print("Document BIO Tags: ", test_sample["doc_bio_tags"])
print("\n-----------\n")
```
### Keyphrase Generation
```python
# get the dataset only for keyphrase generation
dataset = load_dataset("midas/kdd", "generation")
print("Samples for Keyphrase Generation")
# sample from the test split
print("Sample from test data split")
test_sample = dataset["test"][0]
print("Fields in the sample: ", [key for key in test_sample.keys()])
print("Tokenized Document: ", test_sample["document"])
print("Extractive/present Keyphrases: ", test_sample["extractive_keyphrases"])
print("Abstractive/absent Keyphrases: ", test_sample["abstractive_keyphrases"])
print("\n-----------\n")
```
## Citation Information
```
@inproceedings{caragea-etal-2014-citation,
title = "Citation-Enhanced Keyphrase Extraction from Research Papers: A Supervised Approach",
author = "Caragea, Cornelia and
Bulgarov, Florin Adrian and
Godea, Andreea and
Das Gollapalli, Sujatha",
booktitle = "Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing ({EMNLP})",
month = oct,
year = "2014",
address = "Doha, Qatar",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D14-1150",
doi = "10.3115/v1/D14-1150",
pages = "1435--1446",
}
```
## Contributions
Thanks to [@debanjanbhucs](https://github.com/debanjanbhucs), [@dibyaaaaax](https://github.com/dibyaaaaax) and [@ad6398](https://github.com/ad6398) for adding this dataset
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