Hugging Face's logo

Datasets:
midas
/
www

Modalities:
Text
Size:
1K - 10K
Libraries:
Datasets
Dataset card Viewer Files Community
www / README.md
umagunturi's picture
umagunturi
Update README.md
e1242dc
preview code
|
raw
history blame contribute delete
7.23 kB

Dataset Summary

A dataset for benchmarking keyphrase extraction and generation techniques from abstract of english scientific articles. For more details about the dataset please refer the original paper - https://aclanthology.org/D14-1150/

Original source of the data -

Dataset Structure

Table 1: Statistics on the length of the abstractive keyphrases for Test split of www dataset.

Test
Single word 28.21%
Two words 47.65%
Three words 15.20%
Four words 8.04%
Five words 0.65%
Six words 0.12%
Seven words 0.05%
Eight words 0.05%

Table 2: Statistics on the length of the extractive keyphrases for Test split of www dataset.

Test
Single word 44.09%
Two words 48.07%
Three words 7.20%
Four words 0.45%
Five words 0.16%

Table 3: General statistics about www dataset.

Type of Analysis Test
Annotator Type Authors and Readers
Document Type Scientific Articles
No. of Documents 1330
Avg. Document length (words) 163.51
Max Document length (words) 587
Max no. of abstractive keyphrases in a document 13
Min no. of abstractive keyphrases in a document 0
Avg. no. of abstractive keyphrases per document 2.98
Max no. of extractive keyphrases in a document 9
Min no. of extractive keyphrases in a document 0
Avg. no. of extractive keyphrases per document 1.81

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 1330

Usage

Full Dataset 

from datasets import load_dataset

# get entire dataset
dataset = load_dataset("midas/www", "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:  ['The', 'web', 'of', 'nations', 'In', 'this', 'paper', ',', 'we', 'report', 'on', 'a', 'large-scale', 'study', 'of', 'structural', 'differences', 'among', 'the', 'national', 'webs', '.', 'The', 'study', 'is', 'based', 'on', 'a', 'web-scale', 'crawl', 'conducted', 'in', 'the', 'summer', '2008', '.', 'More', 'specifically', ',', 'we', 'study', 'two', 'graphs', 'derived', 'from', 'this', 'crawl', ',', 'the', 'nation', 'graph', ',', 'with', 'nodes', 'corresponding', 'to', 'nations', 'and', 'edges', '-', 'to', 'links', 'among', 'nations', ',', 'and', 'the', 'host', 'graph', ',', 'with', 'nodes', 'corresponding', 'to', 'hosts', 'and', 'edges', '-', 'to', 'hyperlinks', 'among', 'pages', 'on', 'the', 'hosts', '.', 'Contrary', 'to', 'some', 'of', 'the', 'previous', 'work', '(', '2', ')', ',', 'our', 'results', 'show', 'that', 'webs', 'of', 'different', 'nations', 'are', 'often', 'very', 'different', 'from', 'each', 'other', ',', 'both', 'in', 'terms', 'of', 'their', 'internal', 'structure', ',', 'and', 'in', 'terms', 'of', 'their', 'connectivity', 'with', 'other', 'nations', '.']
Document BIO Tags:  ['O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'I', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'I', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O']
Extractive/present Keyphrases:  ['host graph', 'nation graph']
Abstractive/absent Keyphrases:  ['web graph', 'web structure']

-----------

Keyphrase Extraction 

from datasets import load_dataset

# get the dataset only for keyphrase extraction
dataset = load_dataset("midas/www", "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/www", "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, @dibyaaaaax and @ad6398 for adding this dataset