Create README.md

README.md

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+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 | 1,296,613 |
+| Test | 10,000 |
+| Validation | 10,000 |
+
+## Usage
+
+### Small Dataset
+
+```python
+from datasets import load_dataset
+
+# get small dataset
+dataset = load_dataset("midas/ldkp10k", "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/ldkp10k", "medium")
+```
+
+### Large Dataset
+
+```python
+from datasets import load_dataset
+
+# get large dataset
+dataset = load_dataset("midas/ldkp10k", "large")
+```
+
+## Citation Information
+Please cite the works below if you use this dataset in your work.
+
+```
+```
+
+## 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