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--- |
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license: cc |
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task_categories: |
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- text-generation |
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- text2text-generation |
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language: |
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- en |
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tags: |
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- keyphrase-generation |
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- domain-adaptation |
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- paleontology |
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- astrophysics |
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- natural-language-processing |
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size_categories: |
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- 1K<n<10K |
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configs: |
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- config_name: paleo |
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data_files: |
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- split: train |
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path: |
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- "paleo/train.jsonl" |
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- split: test |
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path: "paleo/test.jsonl" |
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- config_name: nlp |
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data_files: |
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- split: train |
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path: |
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- "nlp/train.jsonl" |
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- split: test |
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path: "nlp/test.jsonl" |
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- config_name: astro |
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data_files: |
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- split: train |
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path: |
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- "astro/train.jsonl" |
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- split: test |
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path: "astro/test.jsonl" |
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--- |
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# `silk` synthetic training samples and human-labeled test sets for domain adaptation in keyphrase generation |
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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. |
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We applied `silk` on three domains: Natural Language Processing (nlp), Astrophysics (astro) and Paleontology (paleo). |
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This dataset also includes three human-labeled test sets to assess the performance of keyphrase generation across these domains. |
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## Citation |
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If you use this dataset, please cite the following paper: |
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``` |
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Florian Boudin and Akiko Aizawa. |
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Unsupervised Domain Adaptation for Keyphrase Generation using Citation Context, |
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Proceedings of EMNLP 2024 (Findings). |
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``` |
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