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About this paper
Mingyang Song, Liping Jing, Lin Xiao
Researchers at Beijing Jiaotong University developed KIEMP, a multi-task learning framework for keyphrase extraction that simultaneously considers syntactic accuracy, information saliency, and concept consistency. This approach consistently outperformed state-of-the-art baselines across six diverse datasets, demonstrating improved robustness and generalizability.
Key insights
- A comprehensive multi-perspective approach, integrating syntactic accuracy, information saliency, and concept consistency, provides a more 'human-like' and robust understanding of keyphrase importance.
- Multi-task learning allows these different perspectives to mutually enhance each other during training, leading to a more balanced and effective overall importance score for keyphrase candidates.
- A compact latent space (e.g., 64 dimensions) for learning concept representations is sufficient and effective for capturing high-level conceptual information, contributing to the model's robustness.
Summary by alphaXiv.
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