One2set + Large Language Model: Best Partners for Keyphrase Generation

• URL: http://arxiv.org/abs/2410.03421v2
• Date: Mon, 21 Oct 2024 02:43:50 GMT
• Title: One2set + Large Language Model: Best Partners for Keyphrase Generation
• Authors: Liangying Shao, Liang Zhang, Minlong Peng, Guoqi Ma, Hao Yue, Mingming Sun, Jinsong Su,
• Abstract summary: Keyphrase generation (KPG) aims to automatically generate a collection of phrases representing the core concepts of a given document. We introduce a generate-then-select framework decomposing KPG into two steps, where we adopt a one2set-based model as generator to produce candidates and then use an LLM as selector to select keyphrases from these candidates. Our framework significantly surpasses state-of-the-art models, especially in absent keyphrase prediction.
• Score: 42.969689556605005
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Keyphrase generation (KPG) aims to automatically generate a collection of phrases representing the core concepts of a given document. The dominant paradigms in KPG include one2seq and one2set. Recently, there has been increasing interest in applying large language models (LLMs) to KPG. Our preliminary experiments reveal that it is challenging for a single model to excel in both recall and precision. Further analysis shows that: 1) the one2set paradigm owns the advantage of high recall, but suffers from improper assignments of supervision signals during training; 2) LLMs are powerful in keyphrase selection, but existing selection methods often make redundant selections. Given these observations, we introduce a generate-then-select framework decomposing KPG into two steps, where we adopt a one2set-based model as generator to produce candidates and then use an LLM as selector to select keyphrases from these candidates. Particularly, we make two important improvements on our generator and selector: 1) we design an Optimal Transport-based assignment strategy to address the above improper assignments; 2) we model the keyphrase selection as a sequence labeling task to alleviate redundant selections. Experimental results on multiple benchmark datasets show that our framework significantly surpasses state-of-the-art models, especially in absent keyphrase prediction.

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