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.

翻译：关键词提取是自然语言处理中的核心任务之一。经典提取模型以其较短的注意力范围而著称，这使得它们难以建立相距较远的单词与句子之间的关联关系。这进而限制了它们在生成需从全文语境推断的关键词时的实用性。本文探索利用大语言模型从项目的元数据中推断并生成关键词。我们的建模框架包含多个阶段，通过避免输出无信息量或敏感的关键词，并减少大语言模型中常见的幻觉现象，对结果进行精细化调整。我们将该基于大语言模型的框架称为主题感知关键词提取。我们提出了该框架的两种变体，分别用于生成电商场景下产品的抽取式主题与抽象式主题。我们在三个真实数据集上进行了大量实验，结果表明，与基准模型相比，我们的建模框架能够基于准确性和多样性指标提升性能。