Unsupervised Automated Keyphrase Extraction Approaches: A Literature Review

Because of technical improvements and the exponential growth of textual data and digital sources, extracting high-quality keywords at a high level in research is now more challenging. Keyphrase extraction characteristics, which are growing in popularity, are required for high-level keyword extraction. Automated keyphrase extraction (AKE) aims to find the significant subjects of a text document by automatically identifying a small group of single or multi-words from inside the text. AKE is crucial to many NLP and information retrieval tasks, including document summarizing and classification, article recommendation, and full-text indexing. This paper presents a survey of various techniques available for keyphrase extraction, discusses some important feature selection methods utilized by researchers to rank candidate keyphrases based on their importance, and finally debates various benefits and drawbacks for each category.