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 extractio...

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Bibliographic Details
Published in2023 International Conference on Information Technology, Applied Mathematics and Statistics (ICITAMS) pp. 269 - 274
Main Authors Kahdum, Alyaa Abdual, Al-Hameed, Wafaa
Format Conference Proceeding
LanguageEnglish
Published IEEE 20.03.2023
Subjects
Data mining
Feature extraction
Information retrieval
keyphrase
keyphrase extraction
Mathematics
Neural-based Approaches
review
Semantics
Surveys
Syntactics
Traditional Approaches
Unsupervised approach keyphrase extraction
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DOI10.1109/ICITAMS57610.2023.10525639

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Summary: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.
DOI:10.1109/ICITAMS57610.2023.10525639