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A dataset for benchmarking keyphrase extraction and generation techniques from long document English scientific papers. For more details about the dataset please refer the original paper - []().
Data source - []()
## Dataset Summary
## Dataset Structure
### Data Fields
- **id**: unique identifier of the document.
- **sections**: list of all the sections present in the document.
- **sec_text**: list of white space separated list of words present in each section.
- **sec_bio_tags**: list of BIO tags of white space separated list of words present in each section.
- **extractive_keyphrases**: List of all the present keyphrases.
- **abstractive_keyphrase**: List of all the absent keyphrases.
### Data Splits
|Split| #datapoints |
|--|--|
| Train-Small | 20,000 |
| Train-Medium | 50,000 |
| Train-Large | 90,019 |
| Test | 3413 |
| Validation | 3339 |
## Usage
### Small Dataset
```python
from datasets import load_dataset
# get small dataset
dataset = load_dataset("midas/ldkp3k", "small")
def order_sections(sample):
sections = []
sec_text = []
sec_bio_tags = []
if "title" in sample["sections"]:
title_idx = sample["sections"].index("title")
sections.append(sample["sections"].pop(title_idx))
sec_text.append(sample["sec_text"].pop(title_idx))
sec_bio_tags.append(sample["sec_bio_tags"].pop(title_idx))
if "abstract" in sample["sections"]:
abstract_idx = sample["sections"].index("abstract")
sections.append(sample["sections"].pop(abstract_idx))
sec_text.append(sample["sec_text"].pop(abstract_idx))
sec_bio_tags.append(sample["sec_bio_tags"].pop(abstract_idx))
sections += sample["sections"]
sec_text += sample["sec_text"]
sec_bio_tags += sample["sec_bio_tags"]
return sections, sec_text, sec_bio_tags
# sample from the train split
print("Sample from train data split")
train_sample = dataset["train"][0]
sections, sec_text, sec_bio_tags = order_sections(train_sample)
print("Fields in the sample: ", [key for key in train_sample.keys()])
print("Section names: ", sections)
print("Tokenized Document: ", sec_text)
print("Document BIO Tags: ", sec_bio_tags)
print("Extractive/present Keyphrases: ", train_sample["extractive_keyphrases"])
print("Abstractive/absent Keyphrases: ", train_sample["abstractive_keyphrases"])
print("\n-----------\n")
# sample from the validation split
print("Sample from validation data split")
validation_sample = dataset["validation"][0]
sections, sec_text, sec_bio_tags = order_sections(validation_sample)
print("Fields in the sample: ", [key for key in validation_sample.keys()])
print("Section names: ", sections)
print("Tokenized Document: ", sec_text)
print("Document BIO Tags: ", sec_bio_tags)
print("Extractive/present Keyphrases: ", validation_sample["extractive_keyphrases"])
print("Abstractive/absent Keyphrases: ", validation_sample["abstractive_keyphrases"])
print("\n-----------\n")
# sample from the test split
print("Sample from test data split")
test_sample = dataset["test"][0]
sections, sec_text, sec_bio_tags = order_sections(test_sample)
print("Fields in the sample: ", [key for key in test_sample.keys()])
print("Section names: ", sections)
print("Tokenized Document: ", sec_text)
print("Document BIO Tags: ", sec_bio_tags)
print("Extractive/present Keyphrases: ", test_sample["extractive_keyphrases"])
print("Abstractive/absent Keyphrases: ", test_sample["abstractive_keyphrases"])
print("\n-----------\n")
```
**Output**
```bash
```
### Medium Dataset
```python
from datasets import load_dataset
# get medium dataset
dataset = load_dataset("midas/ldkp3k", "medium")
```
### Large Dataset
```python
from datasets import load_dataset
# get large dataset
dataset = load_dataset("midas/ldkp3k", "large")
```
## Citation Information
Please cite the works below if you use this dataset in your work.
```
```
## Contributions
Thanks to [@debanjanbhucs](https://github.com/debanjanbhucs), [@dibyaaaaax](https://github.com/dibyaaaaax), [@UmaGunturi](https://github.com/UmaGunturi) and [@ad6398](https://github.com/ad6398) for adding this dataset
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