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## Dataset Summary |
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Original source - [https://github.com/microsoft/OpenKP](https://github.com/microsoft/OpenKP) |
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## Dataset Structure |
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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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| Train | 134894 | |
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| Test | 6614 | |
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| Validation | 6616 | |
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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/openkp", "raw") |
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# sample from the train split |
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print("Sample from training data split") |
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train_sample = dataset["train"][0] |
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print("Fields in the sample: ", [key for key in train_sample.keys()]) |
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print("Tokenized Document: ", train_sample["document"]) |
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print("Document BIO Tags: ", train_sample["doc_bio_tags"]) |
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print("Extractive/present Keyphrases: ", train_sample["extractive_keyphrases"]) |
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print("Abstractive/absent Keyphrases: ", train_sample["abstractive_keyphrases"]) |
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print("\n-----------\n") |
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# sample from the validation split |
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print("Sample from validation data split") |
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validation_sample = dataset["validation"][0] |
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print("Fields in the sample: ", [key for key in validation_sample.keys()]) |
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print("Tokenized Document: ", validation_sample["document"]) |
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print("Document BIO Tags: ", validation_sample["doc_bio_tags"]) |
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print("Extractive/present Keyphrases: ", validation_sample["extractive_keyphrases"]) |
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print("Abstractive/absent Keyphrases: ", validation_sample["abstractive_keyphrases"]) |
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print("\n-----------\n") |
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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("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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``` |
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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/openkp", "extraction") |
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print("Samples for Keyphrase Extraction") |
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# sample from the train split |
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print("Sample from training data split") |
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train_sample = dataset["train"][0] |
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print("Fields in the sample: ", [key for key in train_sample.keys()]) |
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print("Tokenized Document: ", train_sample["document"]) |
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print("Document BIO Tags: ", train_sample["doc_bio_tags"]) |
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print("\n-----------\n") |
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# sample from the validation split |
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print("Sample from validation data split") |
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validation_sample = dataset["validation"][0] |
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print("Fields in the sample: ", [key for key in validation_sample.keys()]) |
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print("Tokenized Document: ", validation_sample["document"]) |
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print("Document BIO Tags: ", validation_sample["doc_bio_tags"]) |
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print("\n-----------\n") |
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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/openkp", "generation") |
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print("Samples for Keyphrase Generation") |
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# sample from the train split |
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print("Sample from training data split") |
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train_sample = dataset["train"][0] |
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print("Fields in the sample: ", [key for key in train_sample.keys()]) |
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print("Tokenized Document: ", train_sample["document"]) |
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print("Extractive/present Keyphrases: ", train_sample["extractive_keyphrases"]) |
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print("Abstractive/absent Keyphrases: ", train_sample["abstractive_keyphrases"]) |
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print("\n-----------\n") |
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# sample from the validation split |
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print("Sample from validation data split") |
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validation_sample = dataset["validation"][0] |
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print("Fields in the sample: ", [key for key in validation_sample.keys()]) |
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print("Tokenized Document: ", validation_sample["document"]) |
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print("Extractive/present Keyphrases: ", validation_sample["extractive_keyphrases"]) |
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print("Abstractive/absent Keyphrases: ", validation_sample["abstractive_keyphrases"]) |
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print("\n-----------\n") |
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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{Xiong2019OpenDW, |
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title={Open Domain Web Keyphrase Extraction Beyond Language Modeling}, |
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author={Lee Xiong and Chuan Hu and Chenyan Xiong and Daniel Fernando Campos and Arnold Overwijk}, |
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booktitle={EMNLP}, |
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year={2019} |
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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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