adding test/dev

.gitattributes

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 *.mp3 filter=lfs diff=lfs merge=lfs -text
 *.ogg filter=lfs diff=lfs merge=lfs -text
 *.wav filter=lfs diff=lfs merge=lfs -text
+dev.jsonl filter=lfs diff=lfs merge=lfs -text
+test.jsonl filter=lfs diff=lfs merge=lfs -text

.gitignore

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+
+**.DS_Store
+.idea
+.ipynb_checkpoints
+src/

README.md

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+---
+annotations_creators:
+- unknown
+language_creators:
+- unknown
+languages:
+- en
+licenses:
+- cc-by-4-0
+multilinguality:
+- monolingual
+task_categories:
+- text-mining
+- text-generation
+task_ids:
+- keyphrase-generation
+- keyphrase-extraction
+size_categories:
+- 100K<n<1M
+pretty_name: KPTimes
+---
+
+# KPTimes Benchmark Dataset for Keyphrase Generation
+
+## About
+
+KPTimes is a dataset for benchmarking keyphrase extraction and generation models.
+The dataset is composed of 290K news articles in English collected from the [New York Times](https://www.nytimes.com/) and the [Japan
+Times](https://www.japantimes.co.jp/).
+Keyphrases were annotated by editors in a semi-automated manner (that is, editors revise a set of keyphrases proposed by an algorithm and provide additional keyphrases).
+Details about the dataset can be found in the original paper [(Gallina et al., 2019)][gallina-2019].
+
+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 [(Boudin and Gallina, 2021)][boudin-2021].
+
+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.
+Details about the process can be found in `prmu.py`.
+
+## Content and statistics
+
+The dataset contains the following test split:
+
+| Split      | # documents | #words | # keyphrases | % Present | % Reordered | % Mixed | % Unseen |
+| :--------- | ----------: | -----: | -----------: | --------: | ----------: | ------: | -------: |
+| Train      | 259,923     | -  | -         | -     | -        | -    | -     |
+| Validation | 10,000      | -  | -         | -     | -        | -    | -     |
+| Test       | 20,000      | -  | -         | -     | -        | -    | -     |
+
+The following data fields are available :
+
+- **id**: unique identifier of the document.
+- **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.
+- **date**: publishing date (YYYY/MM/DD)
+- **author**: author of the article (<meta name="author"/>)
+- **categories**: categories of the article (1 or 2 categories)
+- **headline**: self-explanatory (<meta property="og:description"/>)
+- **file_name**: last part of the url, this is not a primary key
+- **url**: original url of the document
+
+## References
+
+- (Gallina et al., 2019) Ygor Gallina, Florian Boudin, and Beatrice Daille. 2019.
+  [KPTimes: A Large-Scale Dataset for Keyphrase Generation on News Documents][gallina-2019].
+  In Proceedings of the 12th International Conference on Natural Language Generation, pages 130–135, Tokyo, Japan. Association for Computational Linguistics.
+- (Boudin and Gallina, 2021) Florian Boudin and Ygor Gallina. 2021.
+  [Redefining Absent Keyphrases and their Effect on Retrieval Effectiveness][boudin-2021]. 
+  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.
+
+[gallina-2019]: https://aclanthology.org/W19-8617/
+[boudin-2021]: https://aclanthology.org/2021.naacl-main.330/

dev.jsonl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:da31abf3768ec7acb3dfd5b0f6d6a9761edd232163475af12a528a5e2765975c
+size 51044613

kptimes.py

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+"""KPTimes benchmark dataset for keyphrase extraction an generation."""
+
+
+import csv
+import json
+import os
+
+import datasets
+
+
+# TODO: Add BibTeX citation
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@inproceedings{gallina-etal-2019-kptimes,
+    title = "{KPT}imes: A Large-Scale Dataset for Keyphrase Generation on News Documents",
+    author = "Gallina, Ygor  and
+      Boudin, Florian  and
+      Daille, Beatrice",
+    booktitle = "Proceedings of the 12th International Conference on Natural Language Generation",
+    month = oct # "{--}" # nov,
+    year = "2019",
+    address = "Tokyo, Japan",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/W19-8617",
+    doi = "10.18653/v1/W19-8617",
+    pages = "130--135",
+    abstract = "Keyphrase generation is the task of predicting a set of lexical units that conveys the main content of a source text. Existing datasets for keyphrase generation are only readily available for the scholarly domain and include non-expert annotations. In this paper we present KPTimes, a large-scale dataset of news texts paired with editor-curated keyphrases. Exploring the dataset, we show how editors tag documents, and how their annotations differ from those found in existing datasets. We also train and evaluate state-of-the-art neural keyphrase generation models on KPTimes to gain insights on how well they perform on the news domain. The dataset is available online at https:// github.com/ygorg/KPTimes.",
+}
+"""
+
+# You can copy an official description
+_DESCRIPTION = """\
+KPTimes benchmark dataset for keyphrase extraction an generation.
+"""
+
+# TODO: Add a link to an official homepage for the dataset here
+_HOMEPAGE = "https://aclanthology.org/W03-1028.pdf"
+
+# TODO: Add the licence for the dataset here if you can find it
+_LICENSE = "Apache 2.0 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.jsonl",
+    "train": "train.jsonl",
+    "dev": "dev.jsonl"
+}
+
+
+# TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case
+class KPTimes(datasets.GeneratorBasedBuilder):
+    """TODO: Short description of my dataset."""
+
+    VERSION = datasets.Version("1.1.0")
+
+    # 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
+        if self.config.name == "raw":  # This is the name of the configuration selected in BUILDER_CONFIGS above
+            features = datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "title": datasets.Value("string"),
+                    "abstract": datasets.Value("string"),
+                    "keyphrases": datasets.features.Sequence(datasets.Value("string")),
+                    "prmu": datasets.features.Sequence(datasets.Value("string")),
+                    "date": datasets.Value("string"),
+                    "author": datasets.Value("string"),
+                    "categories": datasets.features.Sequence(datasets.Value("string")),
+                    "headline": datasets.Value("string"),
+                    "file_name": datasets.Value("string"),
+                    "url": 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,  # Here we define them above because they are different between the two configurations
+            # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
+            # specify them. They'll be used if as_supervised=True in builder.as_dataset.
+            # supervised_keys=("sentence", "label"),
+            # Homepage of the dataset for documentation
+            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["dev"]),
+                    "split": "dev",
+                },
+            ),
+        ]
+
+    # 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"],
+                    "date": data["date"],
+                    "author": data["author"],
+                    "categories": data["categories"],
+                    "headline": data["headline"],
+                    "file_name": data["file_name"],
+                    "url": data["url"],
+                }
+

test.jsonl

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+size 85085194