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README.md

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----
-annotations_creators:
-- unknown
-language_creators:
-- unknown
-language:
-- en
-license:
-- unknown
-multilinguality:
-- monolingual
-task_categories:
-- text-mining
-- text-generation
-task_ids:
-- keyphrase-generation
-- keyphrase-extraction
-size_categories:
-- 100K<n<1M
-pretty_name: KP20k
----
-
-# KP20k Benchmark Dataset for Keyphrase Generation
-
-## About
-
-KP20k is a dataset for benchmarking keyphrase extraction and generation models.
-The data is composed of 570 809 abstracts and their associated titles from scientific articles.
-
-Details about the dataset can be found in the original paper:
-- Meng et al 2017.
-  [Deep keyphrase Generation](https://aclanthology.org/P17-1054.pdf)
-  Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, pages 582–592
-  
-Reference (indexer-assigned) keyphrases are also categorized under the PRMU (<u>P</u>resent-<u>R</u>eordered-<u>M</u>ixed-<u>U</u>nseen) scheme as proposed in the following paper:
- - Florian Boudin and Ygor Gallina. 2021.
-   [Redefining Absent Keyphrases and their Effect on Retrieval Effectiveness](https://aclanthology.org/2021.naacl-main.330/). 
-   In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4185–4193, Online. Association for Computational Linguistics.
-   
-Text pre-processing (tokenization) is carried out using spacy (en_core_web_sm model) with a special rule to avoid splitting words with hyphens (e.g. graph-based is kept as one token). Stemming (Porter's stemmer implementation provided in nltk) is applied before reference keyphrases are matched against the source text.   
-  
-## Content
-
-The dataset is divided into the following three splits:
-
-| Split      | # documents | # keyphrases by document (average) | % Present | % Reordered | % Mixed | % Unseen |
-| :--------- | ----------: | -----------:                       | --------: | ----------: | ------: | -------: |
-| Train      | 530 809     |                5.29                |   58.19   | 10.93       | 17.36   |   13.52  |
-| Test       | 20 000      |                5.28                |   58.40   | 10.84       | 17.20   |   13.56  |
-| Validation | 20 000      |                5.27                |   58.20   | 10.94       | 17.26   |   13.61  |
-
-
-The following data fields are available:
-- **id**: unique identifier of the document. **NB** There were no ids in the original dataset. The ids were generated using the python module shortuuid (https://pypi.org/project/shortuuid/)
-- **title**: title of the document.
-- **abstract**: abstract of the document.
-- **keyphrases**: list of reference keyphrases.
-- **prmu**: list of <u>P</u>resent-<u>R</u>eordered-<u>M</u>ixed-<u>U</u>nseen categories for reference keyphrases.
-
-**NB**: The present keyphrases (represented by the "P" label in the PRMU column) are sorted by their apparition order in the text (title + abstract).

dataset_infos.json

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-{"raw": {"description": "KP20k dataset for keyphrase extraction and generation in scientific paper.\n", "citation": "@InProceedings{meng-EtAl:2017:Long,\n  author    = {Meng, Rui  and  Zhao, Sanqiang  and  Han, Shuguang  and  He, Daqing  and  Brusilovsky, Peter  and  Chi, Yu},\n  title     = {Deep Keyphrase Generation},\n  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},\n  month     = {July},\n  year      = {2017},\n  address   = {Vancouver, Canada},\n  publisher = {Association for Computational Linguistics},\n  pages     = {582--592},\n  url       = {http://aclweb.org/anthology/P17-1054}\n}\n", "homepage": "http://memray.me/uploads/acl17-keyphrase-generation.pdf", "license": "MIT LICENSE", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "title": {"dtype": "string", "id": null, "_type": "Value"}, "abstract": {"dtype": "string", "id": null, "_type": "Value"}, "keyphrases": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "prmu": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "kp20k", "config_name": "raw", "version": {"version_str": "0.0.1", "description": "", "major": 0, "minor": 0, "patch": 1}, "splits": {"train": {"name": "train", "num_bytes": 654745019, "num_examples": 530809, "dataset_name": "kp20k"}, "test": {"name": "test", "num_bytes": 24676939, "num_examples": 20000, "dataset_name": "kp20k"}, "validation": {"name": "validation", "num_bytes": 24658967, "num_examples": 20000, "dataset_name": "kp20k"}}, "download_checksums": {"test.json": {"num_bytes": 25256561, "checksum": "bde2d949cc8767c8ec4b3fbc6d25d1d218d1397cba58d130c33d17fd22af25cf"}, "train.json": {"num_bytes": 670114113, "checksum": "5ae4196410dc1e336de4b79e76800bb72c3669ce1888fab0ff46a431c7277c95"}, "validation.json": {"num_bytes": 25238622, "checksum": "a9dd61ed2547485a146b880e84042d002c3ed0fc668d9d0a08631f15e772f691"}}, "download_size": 720609296, "post_processing_size": null, "dataset_size": 704080925, "size_in_bytes": 1424690221}}

kp20k.py

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-import csv
-import json
-import os
-import datasets
-
-
-_CITATION = """\
-@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}
-}
-"""
-
-# You can copy an official description
-_DESCRIPTION = """\
-KP20k dataset for keyphrase extraction and generation in scientific paper.
-"""
-
-_HOMEPAGE = "http://memray.me/uploads/acl17-keyphrase-generation.pdf"
-
-# License information from the original source page https://github.com/memray/seq2seq-keyphrase
-_LICENSE = "MIT LICENSE"
-
-# TODO: Add link to the official dataset URLs here
-# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
-# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
-_URLS = {
-    "test": "test.json",
-    "train": "train.json",
-    "validation": "validation.json"
-}
-
-
-
-# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
-class KP20k(datasets.GeneratorBasedBuilder):
-
-    VERSION = datasets.Version("0.0.1","")
-
-    # This is an example of a dataset with multiple configurations.
-    # If you don't want/need to define several sub-sets in your dataset,
-    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
-
-    # If you need to make complex sub-parts in the datasets with configurable options
-    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
-    # BUILDER_CONFIG_CLASS = MyBuilderConfig
-
-    # You will be able to load one or the other configurations in the following list with
-    # data = datasets.load_dataset('my_dataset', 'first_domain')
-    # data = datasets.load_dataset('my_dataset', 'second_domain')
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(name="raw", version=VERSION, description="This part of my dataset covers the raw data"),
-    ]
-
-    #DEFAULT_CONFIG_NAME = "raw"  # It's not mandatory to have a default configuration. Just use one if it make sense.
-
-    def _info(self):
-        # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
-        print(self.config)
-        features = datasets.Features(
-            {
-                'id': datasets.Value(dtype="string"),
-                "title": datasets.Value("string"),
-                "abstract": datasets.Value("string"),
-                "keyphrases": datasets.features.Sequence(datasets.Value("string")),
-                "prmu": 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):
-        # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
-        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
-
-        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
-        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
-        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
-        urls = _URLS
-        data_dir = dl_manager.download_and_extract(urls)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": os.path.join(data_dir["train"]),
-                    "split": "train",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": os.path.join(data_dir["test"]),
-                    "split": "test"
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": os.path.join(data_dir["validation"]),
-                    "split": "validation",
-                },
-            ),
-        ]
-
-    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
-    def _generate_examples(self, filepath, split):
-        # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
-        # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
-        with open(filepath, encoding="utf-8") as f:
-            for key, row in enumerate(f):
-                data = json.loads(row)
-                # Yields examples as (key, example) tuples
-                yield key, {
-                    "id": data["id"],
-                    "title": data["title"],
-                    "abstract": data["abstract"],
-                    "keyphrases":  data["keyphrases"],
-                    "prmu": data["prmu"],
-                }

validation.json → raw/kp20k-test.parquet

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train.json → raw/kp20k-train-00000-of-00002.parquet

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test.json → raw/kp20k-train-00001-of-00002.parquet

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raw/kp20k-validation.parquet

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