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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://dl.acm.org/doi/10.5555/1620163.1620205](https://dl.acm.org/doi/10.5555/1620163.1620205)
-
-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  |
-|--|--|
-| Test | 308 |
-
-
-## Usage
-
-### Full Dataset
-
-```python
-from datasets import load_dataset
-
-# get entire dataset
-dataset = load_dataset("midas/duc2001", "raw")
-
-# 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
-Sample from test data split
-Fields in the sample:  ['id', 'document', 'doc_bio_tags', 'extractive_keyphrases', 'abstractive_keyphrases', 'other_metadata']
-Tokenized Document:  ['Here', ',', 'at', 'a', 'glance', ',', 'are', 'developments', 'today', 'involving', 'the', 'crash', 'of', 'Pan', 'American', 'World', 'Airways', 'Flight', '103', 'Wednesday', 'night', 'in', 'Lockerbie', ',', 'Scotland', ',', 'that', 'killed', 'all', '259', 'people', 'aboard', 'and', 'more', 'than', '20', 'people', 'on', 'the', 'ground', ':']
-Document BIO Tags:  ['O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'B', 'O', 'B', 'I', 'I', 'I', 'I', 'I', 'O', 'O', 'O', 'B', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O']
-Extractive/present Keyphrases:  ['pan american world airways flight 103', 'crash', 'lockerbie']
-Abstractive/absent Keyphrases:  ['terrorist threats', 'widespread wreckage', 'radical palestinian faction', 'terrorist bombing', 'bomb threat', 'sabotage']
-
------------
-```
-
-### Keyphrase Extraction
-```python
-from datasets import load_dataset
-
-# get the dataset only for keyphrase extraction
-dataset = load_dataset("midas/duc2001", "extraction")
-
-print("Samples for Keyphrase Extraction")
-
-# 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/duc2001", "generation")
-
-print("Samples for Keyphrase Generation")
-
-# 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{10.5555/1620163.1620205,
-author = {Wan, Xiaojun and Xiao, Jianguo},
-title = {Single Document Keyphrase Extraction Using Neighborhood Knowledge},
-year = {2008},
-isbn = {9781577353683},
-publisher = {AAAI Press},
-booktitle = {Proceedings of the 23rd National Conference on Artificial Intelligence - Volume 2},
-pages = {855–860},
-numpages = {6},
-location = {Chicago, Illinois},
-series = {AAAI'08}
-}
-```
-
-## Contributions
-Thanks to [@debanjanbhucs](https://github.com/debanjanbhucs), [@dibyaaaaax](https://github.com/dibyaaaaax) and [@ad6398](https://github.com/ad6398) for adding this dataset

duc2001.py

@@ -1,142 +0,0 @@
-import json
-import datasets
-
-# _SPLIT = ['test']
-_CITATION = """\
-@inproceedings{10.5555/1620163.1620205,
-author = {Wan, Xiaojun and Xiao, Jianguo},
-title = {Single Document Keyphrase Extraction Using Neighborhood Knowledge},
-year = {2008},
-isbn = {9781577353683},
-publisher = {AAAI Press},
-booktitle = {Proceedings of the 23rd National Conference on Artificial Intelligence - Volume 2},
-pages = {855–860},
-numpages = {6},
-location = {Chicago, Illinois},
-series = {AAAI'08}
-}
-"""
-
-_DESCRIPTION = """\
-
-"""
-
-_HOMEPAGE = ""
-
-# TODO: Add the licence for the dataset here if you can find it
-_LICENSE = ""
-
-# TODO: Add link to the official dataset URLs here
-
-_URLS = {
-    "test": "test.jsonl"
-}
-
-
-# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
-class DUC2001(datasets.GeneratorBasedBuilder):
-    """TODO: Short description of my dataset."""
-
-    VERSION = datasets.Version("0.0.1")
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="extraction", version=VERSION,
-                               description="This part of my dataset covers extraction"),
-        datasets.BuilderConfig(name="generation", version=VERSION,
-                               description="This part of my dataset covers generation"),
-        datasets.BuilderConfig(name="raw", version=VERSION, description="This part of my dataset covers the raw data"),
-    ]
-
-    DEFAULT_CONFIG_NAME = "extraction"
-
-    def _info(self):
-        if self.config.name == "extraction":  # This is the name of the configuration selected in BUILDER_CONFIGS above
-            features = datasets.Features(
-                {
-                    "id": datasets.Value("string"),
-                    "document": datasets.features.Sequence(datasets.Value("string")),
-                    "doc_bio_tags": datasets.features.Sequence(datasets.Value("string"))
-
-                }
-            )
-        elif self.config.name == "generation":
-            features = datasets.Features(
-                {
-                    "id": datasets.Value("string"),
-                    "document": datasets.features.Sequence(datasets.Value("string")),
-                    "extractive_keyphrases": datasets.features.Sequence(datasets.Value("string")),
-                    "abstractive_keyphrases": datasets.features.Sequence(datasets.Value("string"))
-
-                }
-            )
-        else:
-            features = datasets.Features(
-                {
-                    "id": datasets.Value("string"),
-                    "document": datasets.features.Sequence(datasets.Value("string")),
-                    "doc_bio_tags": datasets.features.Sequence(datasets.Value("string")),
-                    "extractive_keyphrases": datasets.features.Sequence(datasets.Value("string")),
-                    "abstractive_keyphrases": datasets.features.Sequence(datasets.Value("string")),
-                    "other_metadata": datasets.features.Sequence(
-                        {
-                            "text": datasets.features.Sequence(datasets.Value("string")),
-                            "bio_tags": datasets.features.Sequence(datasets.Value("string"))
-                        }
-                    )
-
-                }
-            )
-        return datasets.DatasetInfo(
-            # This is the description that will appear on the datasets page.
-            description=_DESCRIPTION,
-            # This defines the different columns of the dataset and their types
-            features=features,
-            homepage=_HOMEPAGE,
-            # License for the dataset if available
-            license=_LICENSE,
-            # Citation for the dataset
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-
-        data_dir = dl_manager.download_and_extract(_URLS)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": data_dir['test'],
-                    "split": "test"
-                },
-            ),
-        ]
-
-    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
-    def _generate_examples(self, filepath, split):
-        with open(filepath, encoding="utf-8") as f:
-            for key, row in enumerate(f):
-                data = json.loads(row)
-                if self.config.name == "extraction":
-                    # Yields examples as (key, example) tuples
-                    yield key, {
-                        "id": data['paper_id'],
-                        "document": data["document"],
-                        "doc_bio_tags": data.get("doc_bio_tags")
-                    }
-                elif self.config.name == "generation":
-                    yield key, {
-                        "id": data['paper_id'],
-                        "document": data["document"],
-                        "extractive_keyphrases": data.get("extractive_keyphrases"),
-                        "abstractive_keyphrases": data.get("abstractive_keyphrases")
-                    }
-                else:
-                    yield key, {
-                        "id": data['paper_id'],
-                        "document": data["document"],
-                        "doc_bio_tags": data.get("doc_bio_tags"),
-                        "extractive_keyphrases": data.get("extractive_keyphrases"),
-                        "abstractive_keyphrases": data.get("abstractive_keyphrases"),
-                        "other_metadata": data["other_metadata"]
-                    }

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