TopS-Key: Semantic Keyphrase Extraction Using Contextual Principal Component Analysis and Fuzzy Technique for Order Preference by Similarity to Ideal Solution With Adaptive Shannon Entropy Weights

Abstract

In the age of rapidly expanding textual data, extracting meaningful insights poses a significant challenge. To address this, we introduce TopS-Key, an advanced framework for automatic keyphrase extraction that integrates natural language processing, principal component analysis (PCA), and fuzzy decision-making method, fuzzy technique for order preference by similarity to ideal solution (Fuzzy TOPSIS). Our approach enhances traditional preprocessing by incorporating fuzzy string matching and normalization. To identify the most important semantic keyphrases, we calculate 10 feature scores and apply PCA to reduce the dimensionality of these features while preserving essential information. The Fuzzy TOPSIS method is used to rank keyphrases, treating keyphrase candidates as alternatives and principal components as evaluation criteria. Shannon entropy-based weighting is applied to determine the significance of each criterion in the Fuzzy TOPSIS process. We validate our framework using widely recognized datasets, including DUC 2001, SemEval 2017 Task 10, and Inspec, leveraging similarity and threshold functions. Evaluation metrics, precision, recall, and the F1 score are analyzed, adjusting thresholds to extract the top 3, 5, and 10 keyphrases. Experimental results show that our TopS-Key framework consistently surpasses existing methods across all datasets, demonstrating superior performance in keyphrase extraction. Furthermore, the TopS-Key model shows significant potential for applications in automated text summarization, information retrieval, and enhanced semantic document analysis, opening avenues for further exploration. Future research could focus on exploring alternative weighting methods for criteria in Fuzzy TOPSIS and applying the framework to other domains with unique challenges in keyphrase extraction.