Dataset Summary
A dataset for benchmarking keyphrase extraction and generation techniques from english news articles. For more details about the dataset please refer the original paper - https://dl.acm.org/doi/10.5555/1620163.1620205
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 | 308 |
Usage
Full Dataset
from datasets import load_dataset
# get entire dataset
dataset = load_dataset("midas/duc2001", "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
Sample from test data split
Fields in the sample: ['id', 'document', 'doc_bio_tags', 'extractive_keyphrases', 'abstractive_keyphrases', 'other_metadata']
Tokenized Document: ['Here', ',', 'at', 'a', 'glance', ',', 'are', 'developments', 'today', 'involving', 'the', 'crash', 'of', 'Pan', 'American', 'World', 'Airways', 'Flight', '103', 'Wednesday', 'night', 'in', 'Lockerbie', ',', 'Scotland', ',', 'that', 'killed', 'all', '259', 'people', 'aboard', 'and', 'more', 'than', '20', 'people', 'on', 'the', 'ground', ':']
Document BIO Tags: ['O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'B', 'I', 'I', 'I', 'I', 'I', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O']
Extractive/present Keyphrases: ['pan american world airways flight 103', 'crash', 'lockerbie']
Abstractive/absent Keyphrases: ['terrorist threats', 'widespread wreckage', 'radical palestinian faction', 'terrorist bombing', 'bomb threat', 'sabotage']
-----------
Keyphrase Extraction
from datasets import load_dataset
# get the dataset only for keyphrase extraction
dataset = load_dataset("midas/duc2001", "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/duc2001", "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{10.5555/1620163.1620205,
author = {Wan, Xiaojun and Xiao, Jianguo},
title = {Single Document Keyphrase Extraction Using Neighborhood Knowledge},
year = {2008},
isbn = {9781577353683},
publisher = {AAAI Press},
booktitle = {Proceedings of the 23rd National Conference on Artificial Intelligence - Volume 2},
pages = {855–860},
numpages = {6},
location = {Chicago, Illinois},
series = {AAAI'08}
}
Contributions
Thanks to @debanjanbhucs, @dibyaaaaax and @ad6398 for adding this dataset