| A dataset for benchmarking keyphrase extraction and generation techniques from long document English scientific papers. For more details about the dataset please refer the original paper - [](). | |
| Data source - []() | |
| ## Dataset Summary | |
| ## Dataset Structure | |
| ### Data Fields | |
| - **id**: unique identifier of the document. | |
| - **sections**: list of all the sections present in the document. | |
| - **sec_text**: list of white space separated list of words present in each section. | |
| - **sec_bio_tags**: list of BIO tags of white space separated list of words present in each section. | |
| - **extractive_keyphrases**: List of all the present keyphrases. | |
| - **abstractive_keyphrase**: List of all the absent keyphrases. | |
| ### Data Splits | |
| |Split| #datapoints | | |
| |--|--| | |
| | Train-Small | 20,000 | | |
| | Train-Medium | 50,000 | | |
| | Train-Large | 90,019 | | |
| | Test | 3413 | | |
| | Validation | 3339 | | |
| ## Usage | |
| ### Small Dataset | |
| ```python | |
| from datasets import load_dataset | |
| # get small dataset | |
| dataset = load_dataset("midas/ldkp3k", "small") | |
| def order_sections(sample): | |
| """ | |
| corrects the order in which different sections appear in the document. | |
| resulting order is: title, abstract, other sections in the body | |
| """ | |
| sections = [] | |
| sec_text = [] | |
| sec_bio_tags = [] | |
| if "title" in sample["sections"]: | |
| title_idx = sample["sections"].index("title") | |
| sections.append(sample["sections"].pop(title_idx)) | |
| sec_text.append(sample["sec_text"].pop(title_idx)) | |
| sec_bio_tags.append(sample["sec_bio_tags"].pop(title_idx)) | |
| if "abstract" in sample["sections"]: | |
| abstract_idx = sample["sections"].index("abstract") | |
| sections.append(sample["sections"].pop(abstract_idx)) | |
| sec_text.append(sample["sec_text"].pop(abstract_idx)) | |
| sec_bio_tags.append(sample["sec_bio_tags"].pop(abstract_idx)) | |
| sections += sample["sections"] | |
| sec_text += sample["sec_text"] | |
| sec_bio_tags += sample["sec_bio_tags"] | |
| return sections, sec_text, sec_bio_tags | |
| # sample from the train split | |
| print("Sample from train data split") | |
| train_sample = dataset["train"][0] | |
| sections, sec_text, sec_bio_tags = order_sections(train_sample) | |
| print("Fields in the sample: ", [key for key in train_sample.keys()]) | |
| print("Section names: ", sections) | |
| print("Tokenized Document: ", sec_text) | |
| print("Document BIO Tags: ", sec_bio_tags) | |
| print("Extractive/present Keyphrases: ", train_sample["extractive_keyphrases"]) | |
| print("Abstractive/absent Keyphrases: ", train_sample["abstractive_keyphrases"]) | |
| print("\n-----------\n") | |
| # sample from the validation split | |
| print("Sample from validation data split") | |
| validation_sample = dataset["validation"][0] | |
| sections, sec_text, sec_bio_tags = order_sections(validation_sample) | |
| print("Fields in the sample: ", [key for key in validation_sample.keys()]) | |
| print("Section names: ", sections) | |
| print("Tokenized Document: ", sec_text) | |
| print("Document BIO Tags: ", sec_bio_tags) | |
| print("Extractive/present Keyphrases: ", validation_sample["extractive_keyphrases"]) | |
| print("Abstractive/absent Keyphrases: ", validation_sample["abstractive_keyphrases"]) | |
| print("\n-----------\n") | |
| # sample from the test split | |
| print("Sample from test data split") | |
| test_sample = dataset["test"][0] | |
| sections, sec_text, sec_bio_tags = order_sections(test_sample) | |
| print("Fields in the sample: ", [key for key in test_sample.keys()]) | |
| print("Section names: ", sections) | |
| print("Tokenized Document: ", sec_text) | |
| print("Document BIO Tags: ", sec_bio_tags) | |
| print("Extractive/present Keyphrases: ", test_sample["extractive_keyphrases"]) | |
| print("Abstractive/absent Keyphrases: ", test_sample["abstractive_keyphrases"]) | |
| print("\n-----------\n") | |
| ``` | |
| **Output** | |
| ```bash | |
| ``` | |
| ### Medium Dataset | |
| ```python | |
| from datasets import load_dataset | |
| # get medium dataset | |
| dataset = load_dataset("midas/ldkp3k", "medium") | |
| ``` | |
| ### Large Dataset | |
| ```python | |
| from datasets import load_dataset | |
| # get large dataset | |
| dataset = load_dataset("midas/ldkp3k", "large") | |
| ``` | |
| ## Citation Information | |
| Please cite the works below if you use this dataset in your work. | |
| ``` | |
| @article{dl4srmahata2022ldkp, | |
| title={LDKP - A Dataset for Identifying Keyphrases from Long Scientific Documents}, | |
| author={Mahata, Debanjan and Agarwal, Naveen and Gautam, Dibya and Kumar, Amardeep and Parekh, Swapnil and Singla, Yaman Kumar and Acharya, Anish and Shah, Rajiv Ratn}, | |
| journal={DL4SR-22: Workshop on Deep Learning for Search and Recommendation, co-located with the 31st ACM International Conference on Information and Knowledge Management (CIKM)}, | |
| address={Atlanta, USA}, | |
| month={October}, | |
| year={2022} | |
| } | |
| ``` | |
| ``` | |
| @article{mahata2022ldkp, | |
| title={LDKP: A Dataset for Identifying Keyphrases from Long Scientific Documents}, | |
| author={Mahata, Debanjan and Agarwal, Naveen and Gautam, Dibya and Kumar, Amardeep and Parekh, Swapnil and Singla, Yaman Kumar and Acharya, Anish and Shah, Rajiv Ratn}, | |
| journal={arXiv preprint arXiv:2203.15349}, | |
| year={2022} | |
| } | |
| ``` | |
| ``` | |
| @article{lo2019s2orc, | |
| title={S2ORC: The semantic scholar open research corpus}, | |
| author={Lo, Kyle and Wang, Lucy Lu and Neumann, Mark and Kinney, Rodney and Weld, Dan S}, | |
| journal={arXiv preprint arXiv:1911.02782}, | |
| year={2019} | |
| } | |
| ``` | |
| ``` | |
| @inproceedings{ccano2019keyphrase, | |
| title={Keyphrase generation: A multi-aspect survey}, | |
| author={{\c{C}}ano, Erion and Bojar, Ond{\v{r}}ej}, | |
| booktitle={2019 25th Conference of Open Innovations Association (FRUCT)}, | |
| pages={85--94}, | |
| year={2019}, | |
| organization={IEEE} | |
| } | |
| ``` | |
| ``` | |
| @article{meng2017deep, | |
| title={Deep keyphrase generation}, | |
| author={Meng, Rui and Zhao, Sanqiang and Han, Shuguang and He, Daqing and Brusilovsky, Peter and Chi, Yu}, | |
| journal={arXiv preprint arXiv:1704.06879}, | |
| year={2017} | |
| } | |
| ``` | |
| ## Contributions | |
| 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 | |