Datasets:
Tasks:
Text Generation
Languages:
English
Multilinguality:
monolingual
Size Categories:
n<1K
Language Creators:
unknown
Annotations Creators:
unknown
Tags:
License:
annotations_creators: | |
- unknown | |
language_creators: | |
- unknown | |
language: | |
- 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 244 **full-text** 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 text 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.s | |
* `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. | |
Titles and abstracts, collected from the [SciCorefCorpus](https://github.com/melsk125/SciCorefCorpus), are also provided. | |
Details about how they were extracted and cleaned up can be found in [(Chaimongkol et al., 2014)][chaimongkol-2014]. | |
Reference keyphrases are provided in stemmed form (because they were provided like this for the test split in the competition). | |
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]. | |
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`. | |
The <u>P</u>resent reference keyphrases are also ordered by their order of apparition in the concatenation of title and text (lvl-1). | |
## Content and statistics | |
The dataset is divided into the following two splits: | |
| Split | # documents | #words | # keyphrases | % Present | % Reordered | % Mixed | % Unseen | | |
| :--------- |------------:|-------:|-------------:|----------:|------------:|--------:|---------:| | |
| Train | 144 | 184.6 | 15.44 | 42.16 | 7.36 | 26.85 | 23.63 | | |
| Test | 100 | 203.1 | 14.66 | 40.11 | 8.34 | 27.12 | 24.43 | | |
Statistics (#words, PRMU distributions) are computed using the title/abstract and not the full text of scientific papers. | |
The following data fields are available : | |
- **id**: unique identifier of the document. | |
- **title**: title of the document. | |
- **abstract**: abstract of the document. | |
- **lvl-1**: content of the document with no text processing. | |
- **lvl-2**: content of the document retrieved from original PDF files and cleaned up. | |
- **lvl-3**: content of the document further abridged to relevant sections. | |
- **lvl-4**: content of the document further abridged using an unsupervised summarization technique. | |
- **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 21β26, Uppsala, Sweden. Association for Computational Linguistics. | |
- (Chaimongkol et al., 2014) Panot Chaimongkol, Akiko Aizawa, and Yuka Tateisi. 2014. | |
[Corpus for Coreference Resolution on Scientific Papers][chaimongkol-2014]. | |
In Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14), pages 3187β3190, Reykjavik, Iceland. European Language Resources Association (ELRA). | |
- (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 121β128, 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 4185β4193, Online. Association for Computational Linguistics. | |
[kim-2010]: https://aclanthology.org/S10-1004/ | |
[chaimongkol-2014]: https://aclanthology.org/L14-1259/ | |
[boudin-2016]: https://aclanthology.org/W16-3917/ | |
[boudin-2021]: https://aclanthology.org/2021.naacl-main.330/ | |