Dataset Summary
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 - https://www.semanticscholar.org/paper/Keyphrase-Extraction-from-Single-Documents-in-the-Schutz/08b75d31a90f206b36e806a7ec372f6f0d12457e
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 | 1320 |
Usage
Full Dataset
from datasets import load_dataset
# get entire dataset
dataset = load_dataset("midas/pubmed", "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
Keyphrase Extraction
from datasets import load_dataset
# get the dataset only for keyphrase extraction
dataset = load_dataset("midas/pubmed", "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
# get the dataset only for keyphrase generation
dataset = load_dataset("midas/pubmed", "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{Schutz2008KeyphraseEF,
title={Keyphrase Extraction from Single Documents in the Open Domain Exploiting Linguistic and Statistical Methods},
author={Alexander Schutz},
year={2008}
}
Contributions
Thanks to @debanjanbhucs, @dibyaaaaax and @ad6398 for adding this dataset