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README.md

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 ---
+annotations_creators:
+- unknown
+language_creators:
+- unknown
+languages:
+- en
 license: cc-by-4.0
+multilinguality:
+- monolingual
+task_categories:
+- text-mining
+- text-generation
+task_ids:
+- keyphrase-generation
+- keyphrase-extraction
+size_categories:
+- n<1K
+pretty_name: Preprocessed SemEval-2010 Benchmark dataset 
 ---
+
+# Preprocessed SemEval-2010 Benchmark dataset for Keyphrase Generation
+
+## About
+
+SemEval-2010 is a dataset for benchmarking keyphrase extraction and generation models.
+The dataset is composed of 144 abstracts of scientific papers collected from the [ACM Digital Library](https://dl.acm.org/).
+Keyphrases were annotated by readers and combined with those provided by the authors.
+Details about the SemEval-2010 dataset can be found in the original paper [(kim et al., 2010)][kim-2010].
+
+This version of the dataset was produced by [(Boudin et al., 2016)][boudin-2016] and provides four increasingly sophisticated levels of document preprocessing:
+
+  * `lvl-1`: default files provided by the SemEval-2010 organizers.
+
+  * `lvl-2`: for each file, we manually retrieved the original PDF file from 
+    the ACM Digital Library. We then extract the enriched textual content of 
+    the PDF files using an Optical Character Recognition (OCR) system and 
+    perform document logical structure detection using ParsCit v110505. We use
+    the detected logical structure to remove author-assigned keyphrases and 
+    select only relevant elements : title, headers, abstract, introduction, 
+    related work, body text and conclusion. We finally apply a systematic 
+    dehyphenation at line breaks.
+
+  * `lvl-3`: we further abridge the input text from level 2 preprocessed 
+    documents to the following~: title, headers, abstract, introduction, 
+    related work, background and conclusion.
+
+  * `lvl-4`: we abridge the input text from level 3 preprocessed documents using
+    an unsupervised summarization technique. We keep the title and abstract
+    and select the most content bearing sentences from the remaining contents.
+
+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 is divided into the following three splits:
+
+| Split      | # documents | #words | # keyphrases | % Present | % Reordered | % Mixed | % Unseen |
+| :--------- |------------:|-------:|-------------:|----------:|------------:|--------:|---------:|
+| Train      |         144 |      - |            - |         - |           - |       - |        - |
+| Test       |         100 |      - |            - |         - |           - |       - |        - |
+
+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.
+
+## References
+
+- (Kim et al., 2010). Su Nam Kim, Olena Medelyan, Min-Yen Kan, and Timothy Baldwin. 2010.
+  [SemEval-2010 Task 5 : Automatic Keyphrase Extraction from Scientific Articles][kim-2010].
+  In Proceedings of the 5th International Workshop on Semantic Evaluation, pages 21–26, Uppsala, Sweden. Association for Computational Linguistics.
+- (Boudin et al., 2016) Florian Boudin, Hugo Mougard, and Damien Cram. 2016.
+  [How Document Pre-processing affects Keyphrase Extraction Performance][boudin-2016].
+  In Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT), pages 121–128, Osaka, Japan. The COLING 2016 Organizing Committee.
+- (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.
+
+[kim-2010]: https://aclanthology.org/S10-1004/
+[boudin-2016]: https://aclanthology.org/W16-3917/
+[boudin-2021]: https://aclanthology.org/2021.naacl-main.330/