Keyword extraction is one of the core tasks in natural language processing.
Classic extraction models are notorious for having a short attention span which
make it hard for them to conclude relational connections among the words and
sentences that are far from each other. This, in turn, makes their usage
prohibitive for generating keywords that are inferred from the context of the
whole text. In this paper, we explore using Large Language Models (LLMs) in
generating keywords for items that are inferred from the items textual
metadata. Our modeling framework includes several stages to fine grain the
results by avoiding outputting keywords that are non informative or sensitive
and reduce hallucinations common in LLM. We call our LLM-based framework
Theme-Aware Keyword Extraction (LLM TAKE). We propose two variations of
framework for generating extractive and abstractive themes for products in an E
commerce setting. We perform an extensive set of experiments on three real data
sets and show that our modeling framework can enhance accuracy based and
diversity based metrics when compared with benchmark models.

利用大型语言模型（LLMs）生成基于上下文的关键词，提出了主题感知关键词提取（LLM TAKE）的框架，通过避免输出非信息或敏感关键词，并减少在 LLMs 中普遍存在的幻觉，为电子商务领域的产品生成提取和抽象主题的两个变体，通过对三个真实数据集的广泛实验证明，该模型能够通过比较基准模型提升准确性和多样性。