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A dataset for benchmarking keyphrase extraction and generation techniques from abstracts of English scientific papers. For more details about the dataset please refer the original paper - [http://memray.me/uploads/acl17-keyphrase-generation.pdf](http://memray.me/uploads/acl17-keyphrase-generation.pdf). |
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Data source - [https://github.com/memray/seq2seq-keyphrase](https://github.com/memray/seq2seq-keyphrase) |
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## Dataset Summary |
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## Dataset Structure |
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## Dataset Statistics |
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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| No. of datapoints | |
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|--|--| |
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| Train | 530,809 | |
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| Test | 20,000| |
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| Validation | 20,000| |
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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/kp20k", "raw") |
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# sample from the train split |
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print("Sample from training dataset 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 dataset 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 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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``` |
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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/kp20k", "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/kp20k", "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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Please cite the works below if you use this dataset in your work. |
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``` |
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@InProceedings{meng-EtAl:2017:Long, |
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author = {Meng, Rui and Zhao, Sanqiang and Han, Shuguang and He, Daqing and Brusilovsky, Peter and Chi, Yu}, |
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title = {Deep Keyphrase Generation}, |
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booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)}, |
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month = {July}, |
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year = {2017}, |
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address = {Vancouver, Canada}, |
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publisher = {Association for Computational Linguistics}, |
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pages = {582--592}, |
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url = {http://aclweb.org/anthology/P17-1054} |
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} |
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@article{mahata2022ldkp, |
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title={LDKP: A Dataset for Identifying Keyphrases from Long Scientific Documents}, |
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author={Mahata, Debanjan and Agarwal, Navneet and Gautam, Dibya and Kumar, Amardeep and Parekh, Swapnil and Singla, Yaman Kumar and Acharya, Anish and Shah, Rajiv Ratn}, |
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journal={arXiv preprint arXiv:2203.15349}, |
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year={2022} |
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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), [@UmaGunturi](https://github.com/UmaGunturi) and [@ad6398](https://github.com/ad6398) for adding this dataset |
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