Upload README.md

README.md

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+## Dataset Summary
+
+A dataset for benchmarking keyphrase extraction and generation techniques from english news articles. For more details about the dataset please refer the original paper - [https://arxiv.org/abs/1306.4886](https://arxiv.org/abs/1306.4886)
+
+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 | 450 |
+| Test | 50 |
+
+
+## Usage
+
+### Full Dataset
+
+```python
+from datasets import load_dataset
+
+# get entire dataset
+dataset = load_dataset("midas/kpcrowd", "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/kpcrowd", "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/kpcrowd", "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
+```
+@misc{marujo2013supervised,
+      title={Supervised Topical Key Phrase Extraction of News Stories using Crowdsourcing, Light Filtering and Co-reference Normalization}, 
+      author={Luis Marujo and Anatole Gershman and Jaime Carbonell and Robert Frederking and João P. Neto},
+      year={2013},
+      eprint={1306.4886},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```
+
+## Contributions
+Thanks to [@debanjanbhucs](https://github.com/debanjanbhucs), [@dibyaaaaax](https://github.com/dibyaaaaax) and [@ad6398](https://github.com/ad6398) for adding this dataset