| ## Dataset Summary | |
| A dataset for benchmarking keyphrase extraction and generation techniques from english scientific papers. For more details about the dataset please refer the original paper - [https://dl.acm.org/doi/abs/10.1145/313238.313437](https://dl.acm.org/doi/abs/10.1145/313238.313437) | |
| Original source of the data - []() | |
| ## Dataset Structure | |
| ### 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 | | |
| |--|--| | |
| | Train | 130 | | |
| | Test | 500 | | |
| ## Usage | |
| ### Full Dataset | |
| ```python | |
| from datasets import load_dataset | |
| # get entire dataset | |
| dataset = load_dataset("midas/cstr", "raw") | |
| # sample from the train split | |
| print("Sample from train dataset split") | |
| test_sample = dataset["train"][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") | |
| # 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** | |
| ```bash | |
| ``` | |
| ### Keyphrase Extraction | |
| ```python | |
| from datasets import load_dataset | |
| # get the dataset only for keyphrase extraction | |
| dataset = load_dataset("midas/cstr", "extraction") | |
| print("Samples for Keyphrase Extraction") | |
| # sample from the train split | |
| print("Sample from train data split") | |
| test_sample = dataset["train"][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") | |
| # 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 | |
| ```python | |
| # get the dataset only for keyphrase generation | |
| dataset = load_dataset("midas/cstr", "generation") | |
| print("Samples for Keyphrase Generation") | |
| # sample from the train split | |
| print("Sample from train data split") | |
| test_sample = dataset["train"][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") | |
| # 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{10.1145/313238.313437, | |
| author = {Witten, Ian H. and Paynter, Gordon W. and Frank, Eibe and Gutwin, Carl and Nevill-Manning, Craig G.}, | |
| title = {KEA: Practical Automatic Keyphrase Extraction}, | |
| year = {1999}, | |
| isbn = {1581131453}, | |
| publisher = {Association for Computing Machinery}, | |
| address = {New York, NY, USA}, | |
| url = {https://doi.org/10.1145/313238.313437}, | |
| doi = {10.1145/313238.313437}, | |
| booktitle = {Proceedings of the Fourth ACM Conference on Digital Libraries}, | |
| pages = {254–255}, | |
| numpages = {2}, | |
| location = {Berkeley, California, USA}, | |
| series = {DL '99} | |
| } | |
| ``` | |
| ## Contributions | |
| Thanks to [@debanjanbhucs](https://github.com/debanjanbhucs), [@dibyaaaaax](https://github.com/dibyaaaaax) and [@ad6398](https://github.com/ad6398) for adding this dataset | |