Create README.md

README.md

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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).
+
+Data source - [https://github.com/memray/seq2seq-keyphrase](https://github.com/memray/seq2seq-keyphrase)
+
+## Dataset Summary
+  
+
+## Dataset Structure
+
+## Dataset Statistics 
+
+
+### 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| No. of datapoints  |
+|--|--|
+| Train | 530,809 |
+| Test | 20,000|
+| Validation | 20,000|
+
+## Usage
+
+### Full Dataset
+
+```python
+from datasets import load_dataset
+
+# get entire dataset
+dataset = load_dataset("midas/kp20k", "raw")
+
+# sample from the train split
+print("Sample from training dataset split")
+train_sample = dataset["train"][0]
+print("Fields in the sample: ", [key for key in train_sample.keys()])
+print("Tokenized Document: ", train_sample["document"])
+print("Document BIO Tags: ", train_sample["doc_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 dataset split")
+validation_sample = dataset["validation"][0]
+print("Fields in the sample: ", [key for key in validation_sample.keys()])
+print("Tokenized Document: ", validation_sample["document"])
+print("Document BIO Tags: ", validation_sample["doc_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 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/kp20k", "extraction")
+
+print("Samples for Keyphrase Extraction")
+
+# sample from the train split
+print("Sample from training data split")
+train_sample = dataset["train"][0]
+print("Fields in the sample: ", [key for key in train_sample.keys()])
+print("Tokenized Document: ", train_sample["document"])
+print("Document BIO Tags: ", train_sample["doc_bio_tags"])
+print("\n-----------\n")
+
+# sample from the validation split
+print("Sample from validation data split")
+validation_sample = dataset["validation"][0]
+print("Fields in the sample: ", [key for key in validation_sample.keys()])
+print("Tokenized Document: ", validation_sample["document"])
+print("Document BIO Tags: ", validation_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/kp20k", "generation")
+
+print("Samples for Keyphrase Generation")
+
+# sample from the train split
+print("Sample from training data split")
+train_sample = dataset["train"][0]
+print("Fields in the sample: ", [key for key in train_sample.keys()])
+print("Tokenized Document: ", train_sample["document"])
+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]
+print("Fields in the sample: ", [key for key in validation_sample.keys()])
+print("Tokenized Document: ", validation_sample["document"])
+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]
+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
+Please cite the works below if you use this dataset in your work.
+
+```
+@InProceedings{meng-EtAl:2017:Long,
+  author    = {Meng, Rui  and  Zhao, Sanqiang  and  Han, Shuguang  and  He, Daqing  and  Brusilovsky, Peter  and  Chi, Yu},
+  title     = {Deep Keyphrase Generation},
+  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
+  month     = {July},
+  year      = {2017},
+  address   = {Vancouver, Canada},
+  publisher = {Association for Computational Linguistics},
+  pages     = {582--592},
+  url       = {http://aclweb.org/anthology/P17-1054}
+}
+```
+
+
+## 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