This repository contains a large, curated set of benchmark datasets for evaluating automatic keyphrase extraction algorithms. These datasets are all pre-processed using the Stanford CoreNLP suite and are available in XML format.

All datasets are stored according to the following, common structure:

dataset/
       /test/       <- test documents
       /train/      <- training documents (if available)
       /dev/        <- validation documents (if available)
       /src/        <- everything used to build the dataset
       /references/ <- reference keyphrases in json format

Bigger datasets (such as KP20k, KPTimes) should be downloaded and preprocessed using the dataset/src directory.

Reference keyphrases, used for evaluating automatic keyphrase extraction algorithms, are available in json format and named according to the following rules: [split].[annotator].[stem]?.json

where

• split corresponds to the dataset split: test, train, dev or valid
• annotator is the type of annotation: author, reader, editor, combined, contr (controlled vocabulary), uncontr (free annotation)
• stem (optional) indicates that stemming (using nltk Porter algorithm) is applied on reference keyphrases.

Below is a an example of reference file format:

{
    "doc-1": [
        [
            "target detect"
        ],
        [
            "number of sensor",
            "sensor number"
        ]
    ],
    ...
}

Annotation for gold keyphrases are performed by authors (A), readers (R), editors (E) or professional indexers (I).

1. KEA: Practical automatic keyphrase extraction. Witten, I. H., Paynter, G. W., Frank, E., Gutwin, C., & Nevill-Manning, C. G. In Proceedings of the fourth ACM conference on Digital libraries. p. 254-255. 1999.

2. Improved automatic keyword extraction given more linguistic knowledge. Anette Hulth. In Proceedings of EMNLP 2003. p. 216-223.

3. Keyphrase Extraction in Scientific Publications. Thuy Dung Nguyen and Min-Yen Kan. In Proceedings of International Conference on Asian Digital Libraries 2007. p. 317-326.

4. Single Document Keyphrase Extraction Using Neighborhood Knowledge. Xiaojun Wan and Jianguo Xiao. In Proceedings of AAAI 2008. pp. 855-860.

5. Keyphrase extraction from single documents in the open domain exploiting linguistic and statistical methods. Alexander Thorsten Schutz. Master's thesis, National University of Ireland (2008).

6. Large dataset for keyphrases extraction. Krapivin, M., Autaeu, A., & Marchese, M. (2009). University of Trento.

7. Supervised Topical Key Phrase Extraction of News Stories using Crowdsourcing, Light Filtering and Co-reference Normalization. Marujo, L., Gershman, A., Carbonell, J., Frederking, R., & Neto, J. P. In Proceedings of LREC 2012.

8. TopicRank: Graph-Based Topic Ranking for Keyphrase Extraction. Adrien Bougouin, Florian Boudin, Béatrice Daille. In Proceedings of the International Joint Conference on Natural Language Processing (IJCNLP), 2013.

9. Citation-Enhanced Keyphrase Extraction from Research Papers: A Supervised Approach. Cornelia Caragea, Florin Bulgarov, Andreea Godea and Sujatha Das Gollapalli. In Proceedings of EMNLP 2014. pp. 1435-1446.

10. How Document Pre-processing affects Keyphrase Extraction Performance. Florian Boudin, Hugo Mougard and Damien Cram. COLING 2016 Workshop on Noisy User-generated Text (WNUT).

11. TermITH-Eval: a French Standard-Based Resource for Keyphrase Extraction Evaluation. Adrien Bougouin, Sabine Barreaux, Laurent Romary, Florian Boudin and​ Béatrice Daille. Language Resources and Evaluation Conference (LREC), 2016.

12. Keyphrase Cloud Generation of Broadcast News. Luis Marujo, Márcio Viveiros, João Paulo da Silva Neto. In Proceedings of Interspeech 2011.

13. Human-competitive tagging using automatic keyphrase extraction. O. Medelyan, E. Frank, I. H. Witten. In Proceedings of EMNLP 2009.

14. TALN Archives: a digital archive of French research articles in Natural Language Processing. Florian Boudin. In Proceedings of TALN 2013.

15. Deep Keyphrase Generation R. Meng, S. Zhao, S. Han, D. He, P. Brusilovsky and Y. Chi. In Proceedings of ACL 2017.

16. KPTimes: A Large-Scale Dataset for Keyphrase Generation on News Documents. Y. Gallina, F. Boudin and B. Daille. In Proceedings of INLG 2019.