REVIEW OF REAL-WORLD APPLICATIONS OF NLP-BASED KEYWORD IDENTIFICATION

Abstract

Keyword extraction is an important task in natural language processing, supporting summarization, indexing, content analysis, and information retrieval. Over time, a wide range of methods have been developed to automate the identification of key concepts in text. This study reviews the main families of keyword extraction techniques, including statistical methods, graph-based algorithms, embedding-based models, and approaches that use neural representations. In addition to summarizing existing research, the study evaluates several widely used tools, such as TF-IDF, YAKE, KeyBERT, KPMiner, TextRank, TopicRank, and MultipartiteRank, and examines how their strengths and limitations appear when applied to real educational materials. Special attention is given to the challenges that arise in multilingual and low-resource settings, particularly for languages with fewer annotated resources. The analysis highlights patterns in algorithm behavior, differences in semantic coverage, and factors that influence performance across syllabus formats and subject areas. The findings aim to help researchers and practitioners choose appropriate keyword extraction methods for educational, multilingual, and domain-specific applications.

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