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## Dataset Summary |
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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/](https://aclanthology.org/D14-1150/) |
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Original source of the data - []() |
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## Dataset Structure |
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Table 1: Statistics on the length of the abstractive keyphrases for Test split of www dataset. |
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| | Test | |
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|:-----------:|:------:| |
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| Single word | 28.21% | |
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| Two words | 47.65% | |
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| Three words | 15.20% | |
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| Four words | 8.04% | |
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| Five words | 0.65% | |
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| Six words | 0.12% | |
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| Seven words | 0.05% | |
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| Eight words | 0.05% | |
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Table 2: Statistics on the length of the extractive keyphrases for Test split of www dataset. |
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| | Test | |
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|:-----------:|:------:| |
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| Single word | 44.09% | |
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| Two words | 48.07% | |
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| Three words | 7.20% | |
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| Four words | 0.45% | |
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| Five words | 0.16% | |
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Table 3: General statistics about www dataset. |
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| Type of Analysis | Test | |
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|:------------------------------------------------:|:-------------------:| |
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| Annotator Type | Authors and Readers | |
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| Document Type | Scientific Articles | |
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| No. of Documents | 1330 | |
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| Avg. Document length (words) | 163.51 | |
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| Max Document length (words) | 587 | |
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| Max no. of abstractive keyphrases in a document | 13 | |
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| Min no. of abstractive keyphrases in a document | 0 | |
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| Avg. no. of abstractive keyphrases per document | 2.98 | |
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| Max no. of extractive keyphrases in a document | 9 | |
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| Min no. of extractive keyphrases in a document | 0 | |
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| Avg. no. of extractive keyphrases per document | 1.81 | |
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### Data Fields |
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- **id**: unique identifier of the document. |
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- **document**: Whitespace separated list of words in the document. |
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- **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. |
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- **extractive_keyphrases**: List of all the present keyphrases. |
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- **abstractive_keyphrase**: List of all the absent keyphrases. |
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### Data Splits |
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|Split| #datapoints | |
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|--|--| |
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| Test | 1330 | |
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## Usage |
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### Full Dataset |
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```python |
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from datasets import load_dataset |
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# get entire dataset |
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dataset = load_dataset("midas/www", "raw") |
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# sample from the test split |
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print("Sample from test dataset split") |
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test_sample = dataset["test"][0] |
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print("Fields in the sample: ", [key for key in test_sample.keys()]) |
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print("Tokenized Document: ", test_sample["document"]) |
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print("Document BIO Tags: ", test_sample["doc_bio_tags"]) |
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print("Extractive/present Keyphrases: ", test_sample["extractive_keyphrases"]) |
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print("Abstractive/absent Keyphrases: ", test_sample["abstractive_keyphrases"]) |
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print("\n-----------\n") |
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``` |
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**Output** |
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```bash |
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Sample from test data split |
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Fields in the sample: ['id', 'document', 'doc_bio_tags', 'extractive_keyphrases', 'abstractive_keyphrases', 'other_metadata'] |
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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', '.'] |
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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'] |
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Extractive/present Keyphrases: ['host graph', 'nation graph'] |
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Abstractive/absent Keyphrases: ['web graph', 'web structure'] |
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----------- |
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``` |
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### Keyphrase Extraction |
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```python |
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from datasets import load_dataset |
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# get the dataset only for keyphrase extraction |
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dataset = load_dataset("midas/www", "extraction") |
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print("Samples for Keyphrase Extraction") |
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# sample from the test split |
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print("Sample from test data split") |
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test_sample = dataset["test"][0] |
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print("Fields in the sample: ", [key for key in test_sample.keys()]) |
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print("Tokenized Document: ", test_sample["document"]) |
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print("Document BIO Tags: ", test_sample["doc_bio_tags"]) |
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print("\n-----------\n") |
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``` |
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### Keyphrase Generation |
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```python |
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# get the dataset only for keyphrase generation |
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dataset = load_dataset("midas/www", "generation") |
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print("Samples for Keyphrase Generation") |
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# sample from the test split |
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print("Sample from test data split") |
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test_sample = dataset["test"][0] |
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print("Fields in the sample: ", [key for key in test_sample.keys()]) |
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print("Tokenized Document: ", test_sample["document"]) |
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print("Extractive/present Keyphrases: ", test_sample["extractive_keyphrases"]) |
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print("Abstractive/absent Keyphrases: ", test_sample["abstractive_keyphrases"]) |
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print("\n-----------\n") |
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``` |
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## Citation Information |
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``` |
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@inproceedings{caragea-etal-2014-citation, |
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title = "Citation-Enhanced Keyphrase Extraction from Research Papers: A Supervised Approach", |
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author = "Caragea, Cornelia and |
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Bulgarov, Florin Adrian and |
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Godea, Andreea and |
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Das Gollapalli, Sujatha", |
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booktitle = "Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing ({EMNLP})", |
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month = oct, |
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year = "2014", |
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address = "Doha, Qatar", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/D14-1150", |
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doi = "10.3115/v1/D14-1150", |
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pages = "1435--1446", |
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} |
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``` |
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## Contributions |
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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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