Large Language Models for Keyword Extraction: A Survey

Keyword extraction is a crucial task in natural language processing, which involves identifying the most important words or phrases in a document to represent its content. Traditionally, statistical and graph-based approaches have been used for keyword extraction, but these methods may not capture complex relationships between words. Recently, large language models have shown potential in language tasks, including keyword extraction. These models can reason and understand context better, making them ideal for extracting keywords from entire texts.
In this article, we explore the traditional approaches to keyword extraction, which rely on statistical features such as word frequency, N-grams, location, and document grammar. These features may not capture the intricate relationships between words in a document. We also discuss modern approaches that use large language models to extract keywords, which have demonstrated better performance in language tasks.
The traditional approaches to keyword extraction are based on statistical methods, which calculate a score for each term in a document using various features. These scores are then ranked, and the top n terms are identified as essential keywords. However, these methods may not capture complex relationships between words, leading to inadequate representation of a document’s content.
In contrast, modern approaches use large language models that have been trained on vast amounts of data. These models can understand context better and reason about language, making them ideal for extracting keywords from entire texts. They have demonstrated better performance in language tasks such as summarization and sentiment analysis, and have shown potential in keyword extraction as well.
In conclusion, traditional approaches to keyword extraction may not capture the complex relationships between words in a document, while modern approaches using large language models can provide more accurate representation of a document’s content. These models have demonstrated better performance in language tasks and hold promise for improving keyword extraction techniques.