| ## Dataset Summary |
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| 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](https://dl.acm.org/doi/10.5555/1620163.1620205) |
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| Original source of the data - []() |
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| ## Dataset Structure |
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| ### Data Fields |
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| - **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. |
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| ### Data Splits |
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| |Split| #datapoints | |
| |--|--| |
| | Test | 308 | |
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| ## Usage |
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| ### Full Dataset |
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| ```python |
| from datasets import load_dataset |
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| # get entire dataset |
| dataset = load_dataset("midas/duc2001", "raw") |
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| # 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** |
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| ```bash |
| 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'] |
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| ----------- |
| ``` |
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| ### Keyphrase Extraction |
| ```python |
| from datasets import load_dataset |
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| # get the dataset only for keyphrase extraction |
| dataset = load_dataset("midas/duc2001", "extraction") |
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| print("Samples for Keyphrase Extraction") |
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| # 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") |
| ``` |
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| ### Keyphrase Generation |
| ```python |
| # get the dataset only for keyphrase generation |
| dataset = load_dataset("midas/duc2001", "generation") |
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| print("Samples for Keyphrase Generation") |
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| # 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") |
| ``` |
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| ## 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} |
| } |
| ``` |
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| ## Contributions |
| 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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