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silk synthetic training samples and human-labeled test sets for domain adaptation in keyphrase generation
This dataset contains the synthetic samples generated by 🧵 silk, a method that leverages citation contexts to create synthetic samples of documents paired with silver-standard keyphrases for adapting keyphrase generation models to new domains.
We applied silk on three domains: Natural Language Processing (nlp), Astrophysics (astro) and Paleontology (paleo).
This dataset also includes three human-labeled test sets to assess the performance of keyphrase generation across these domains.
Citation
If you use this dataset, please cite the following paper:
Florian Boudin and Akiko Aizawa.
Unsupervised Domain Adaptation for Keyphrase Generation using Citation Context,
Proceedings of EMNLP 2024 (Findings).
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