MetaKP: On-Demand Keyphrase Generation

Di Wu, Xiaoxian Shen, and Kai-Wei Chang, in EMNLP-Finding, 2024.

Download the full text

Abstract

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.

Bib Entry

@inproceedings{wu2024metakp,
  title = {MetaKP: On-Demand Keyphrase Generation},
  author = {Wu, Di and Shen, Xiaoxian and Chang, Kai-Wei},
  booktitle = {EMNLP-Finding},
  year = {2024}
}