MetaKP: On-Demand Keyphrase Generation
<|reference_start|>MetaKP: On-Demand Keyphrase Generation: Traditional keyphrase prediction methods predict a single set of keyphrases per document, failing to cater to the diverse needs of users and downstream applications. To bridge the gap, we introduce on-demand keyphrase generation, a novel paradigm that requires keyphrases that conform to specific high-level goals or intents. For this task, we present MetaKP, a large-scale benchmark comprising four datasets, 7500 documents, and 3760 goals across news and biomedical domains with human-annotated keyphrases. Leveraging MetaKP, we design both supervised and unsupervised methods, including a multi-task fine-tuning approach and a self-consistency prompting method with large language models. The results highlight the challenges of supervised fine-tuning, whose performance is not robust to distribution shifts. By contrast, the proposed self-consistency prompting approach greatly improves the performance of large language models, enabling GPT-4o to achieve 0.548 SemF1, surpassing the performance of a fully fine-tuned BART-base model. Finally, we demonstrate the potential of our method to serve as a general NLP infrastructure, exemplified by its application in epidemic event detection from social media.<|reference_end|>
@article{wu2024metakp:,
    title={MetaKP: On-Demand Keyphrase Generation},
    author={Di Wu, Xiaoxian Shen, Kai-Wei Chang},
    journal={arXiv preprint arXiv:2407.00191},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.00191},
    primaryClass={cs.CL}
}