FRAKE: Fusional Real-time Automatic Keyword Extraction
Aidin Zehtab-Salmasi, Mohammad-Reza Feizi-Derakhshi, Mohamad-Ali, Balafar

TL;DR
FRAKE introduces a real-time keyword extraction method combining graph centrality and textural features, significantly outperforming existing techniques across multiple datasets with an average 16.9% F-score improvement.
Contribution
The paper presents a novel fusion-based approach for keyword extraction that integrates graph centrality and textural features for improved accuracy.
Findings
Achieved 16.9% higher F-score on average across datasets.
Outperformed existing keyword extraction methods in all tested datasets.
Effective in multilingual and diverse text corpora.
Abstract
Keyword extraction is the process of identifying the words or phrases that express the main concepts of text to the best of one's ability. Electronic infrastructure creates a considerable amount of text every day and at all times. This massive volume of documents makes it practically impossible for human resources to study and manage them. Nevertheless, the need for these documents to be accessed efficiently and effectively is evident in numerous purposes. A blog, news article, or technical note is considered a relatively long text since the reader aims to learn the subject based on keywords or topics. Our approach consists of a combination of two models: graph centrality features and textural features. The proposed method has been used to extract the best keyword among the candidate keywords with an optimal combination of graph centralities, such as degree, betweenness, eigenvector,…
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Taxonomy
TopicsAdvanced Text Analysis Techniques
