Keyphrase distance analysis technique from news articles as a feature for keyphrase extraction: An unsupervised approach

• Main Authors: Miah, Mohammad Badrul Alam, Suryanti, Awang, Rahman, Md Mustafizur, Sanwar Hosen, A. S. M.
• Format: Article
• Language: English
• Published: The Science and Information (SAI) Organization Limited 2023
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://umpir.ump.edu.my/id/eprint/39116/1/Keyphrase%20distance%20analysis%20technique%20from%20news%20articles%20as%20a%20feature%20for%20keyphrase%20extraction.pdf; http://umpir.ump.edu.my/id/eprint/39116/; https://doi.org/10.14569/IJACSA.2023.01410104; https://doi.org/10.14569/IJACSA.2023.01410104
• Institution: Universiti Malaysia Pahang Al-Sultan Abdullah

Due to the rapid expansion of information and online sources, automatic keyphrase extraction remains an important and challenging problem in the field of current study. The use of keyphrases is extremely beneficial for many tasks, including information retrieval (IR) systems and natural language processing (NLP). It is essential to extract the features of those keyphrases for extracting the most significant keyphrases as well as summarizing the texts to the highest standard. In order to analyze the distance between keyphrases in news articles as a feature of keyphrases, this research proposed a region-based unsupervised keyphrase distance analysis (KDA) technique. The proposed method is broken down into eight steps: gathering data, data preprocessing, data processing, searching keyphrases, distance calculation, averaging distance, curve plotting, and lastly, the curve fitting technique. The proposed approach begins by gathering two distinct datasets containing the news items, which are then used in the data preprocessing step, which makes use of a few preprocessing techniques. This preprocessed data is then employed in the data processing phase, where it is routed to the keyphrase searching, distance computation, and distance averaging phases. Finally, the curve fitting method is used after applying a curve plotting analysis. These two benchmark datasets are then used to evaluate and test the performance of the proposed approach. The proposed approach is then contrasted with different approaches to show how effective, advantageous, and significant it is. The results of the evaluation also proved that the proposed technique considerably improved the efficiency of keyphrase extraction techniques. It produces an F1-score value of 96.91% whereas its present keyphrases are 94.55%.