Research map / Label-efficient and robust learning / Recognition and long-tail
Unsupervised Deep Keyphrase Generation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add the Seq2Seq generation module to phrase-bank extraction (ablation) | AutoKeyG · KP20k, Inspec, Krapivin, NUS, and SemEval | F1@5 present: 22.9 -> 23.4; 29.7 -> 30.3; 15.9 -> 17.1; 20.7 -> 21.8; 16.3 -> 18.7 | Helped |
| add TF-IDF information to embedding-based candidate ranking (ablation) | AutoKeyG · KP20k, Inspec, Krapivin, NUS, and SemEval | F1@5 present: 21.2 -> 23.4; 29.7 -> 30.3; 15.9 -> 17.1; 20.4 -> 21.8; 15.3 -> 18.7 | Helped |
| add the Seq2Seq generation module to phrase-bank extraction (ablation) | AutoKeyG · KP20k, Inspec, Krapivin, NUS, and SemEval | R@20 absent: 2.2 -> 2.5; 1.7 -> 2.1; 4.1 -> 5.4; 2.6 -> 3.2; 0.9 -> 1.1 | Helped |
Closest papers by meaning
- Fast and Constrained Absent Keyphrase Generation by Prompt-Based Learning (AAAI 2022 · similarity 0.90)
- Title-Guided Encoding for Keyphrase Generation (AAAI 2019 · similarity 0.88)
- A Deep Generative Framework for Paraphrase Generation (AAAI 2018 · similarity 0.88)
- A new dataset for multilingual keyphrase generation (NeurIPS 2022 · similarity 0.87)
- Incorporating Expert Knowledge into Keyphrase Extraction (AAAI 2017 · similarity 0.86)
- Retrieval is Accurate Generation (ICLR 2024 · similarity 0.82)
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