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--- |
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annotations_creators: |
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- unknown |
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language_creators: |
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- unknown |
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languages: |
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- en |
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license: cc-by-4.0 |
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multilinguality: |
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- monolingual |
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task_categories: |
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- text-mining |
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- text-generation |
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task_ids: |
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- keyphrase-generation |
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- keyphrase-extraction |
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size_categories: |
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- n<1K |
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pretty_name: Preprocessed SemEval-2010 Benchmark dataset |
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--- |
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# Preprocessed SemEval-2010 Benchmark dataset for Keyphrase Generation |
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## About |
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SemEval-2010 is a dataset for benchmarking keyphrase extraction and generation models. |
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The dataset is composed of 244 **full-text** scientific papers collected from the [ACM Digital Library](https://dl.acm.org/). |
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Keyphrases were annotated by readers and combined with those provided by the authors. |
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Details about the SemEval-2010 dataset can be found in the original paper [(kim et al., 2010)][kim-2010]. |
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This version of the dataset was produced by [(Boudin et al., 2016)][boudin-2016] and provides four increasingly sophisticated levels of document preprocessing: |
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* `lvl-1`: default text files provided by the SemEval-2010 organizers. |
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* `lvl-2`: for each file, we manually retrieved the original PDF file from the ACM Digital Library. |
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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. |
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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. |
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We finally apply a systematic dehyphenation at line breaks. |
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* `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. |
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* `lvl-4`: we abridge the input text from level 3 preprocessed documents using an unsupervised summarization technique. |
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We keep the title and abstract and select the most content bearing sentences from the remaining contents. |
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Titles and abstracts, collected from the [SciCorefCorpus](https://github.com/melsk125/SciCorefCorpus), are also provided. |
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Details about how they were extracted and cleaned up can be found in [(Chaimongkol et al., 2014)][chaimongkol-2014]. |
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Reference keyphrases are provided in stemmed form (because they were provided like this for the test split in the competition). |
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They 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]. |
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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). |
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Stemming (Porter's stemmer implementation provided in `nltk`) is applied before reference keyphrases are matched against the source text. |
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Details about the process can be found in `prmu.py`. |
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## Content and statistics |
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The dataset is divided into the following two splits: |
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| Split | # documents | #words | # keyphrases | % Present | % Reordered | % Mixed | % Unseen | |
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| :--------- |------------:|-------:|-------------:|----------:|------------:|--------:|---------:| |
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| Train | 144 | - | - | - | - | - | - | |
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| Test | 100 | - | - | - | - | - | - | |
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Statistics (#words, PRMU distributions) are computed using the title/abstract and not the full text of scientific papers. |
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The following data fields are available : |
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- **id**: unique identifier of the document. |
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- **title**: title of the document. |
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- **abstract**: abstract of the document. |
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- **lvl-1**: content of the document with no text processing. |
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- **lvl-2**: content of the document retrieved from original PDF files and cleaned up. |
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- **lvl-3**: content of the document further abridged to relevant sections. |
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- **lvl-4**: content of the document further abridged using an unsupervised summarization technique. |
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- **keyphrases**: list of reference keyphrases. |
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- **prmu**: list of <u>P</u>resent-<u>R</u>eordered-<u>M</u>ixed-<u>U</u>nseen categories for reference keyphrases. |
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## References |
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- (Kim et al., 2010) Su Nam Kim, Olena Medelyan, Min-Yen Kan, and Timothy Baldwin. 2010. |
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[SemEval-2010 Task 5 : Automatic Keyphrase Extraction from Scientific Articles][kim-2010]. |
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In Proceedings of the 5th International Workshop on Semantic Evaluation, pages 21–26, Uppsala, Sweden. Association for Computational Linguistics. |
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- (Chaimongkol et al., 2014) Panot Chaimongkol, Akiko Aizawa, and Yuka Tateisi. 2014. |
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[Corpus for Coreference Resolution on Scientific Papers][chaimongkol-2014]. |
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In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14), pages 3187–3190, Reykjavik, Iceland. European Language Resources Association (ELRA). |
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- (Boudin et al., 2016) Florian Boudin, Hugo Mougard, and Damien Cram. 2016. |
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[How Document Pre-processing affects Keyphrase Extraction Performance][boudin-2016]. |
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In Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT), pages 121–128, Osaka, Japan. The COLING 2016 Organizing Committee. |
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- (Boudin and Gallina, 2021) Florian Boudin and Ygor Gallina. 2021. |
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[Redefining Absent Keyphrases and their Effect on Retrieval Effectiveness][boudin-2021]. |
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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. |
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[kim-2010]: https://aclanthology.org/S10-1004/ |
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[chaimongkol-2014]: https://aclanthology.org/L14-1259/ |
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[boudin-2016]: https://aclanthology.org/W16-3917/ |
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[boudin-2021]: https://aclanthology.org/2021.naacl-main.330/ |
