Keyphrases Concentrated Area Identification from Academic Articles as Feature of Keyphrase Extraction: A New Unsupervised Approach

• Main Authors: Miah, Mohammad Badrul Alam, Suryanti, Awang, Azad, Md Saiful, Rahman, Md Mustafizur
• Format: Article
• Language: English
• Published: The Science and Information (SAI) Organization Limited 2022
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://umpir.ump.edu.my/id/eprint/33320/1/Keyphrases%20Concentrated%20Area%20Identification.pdf; http://umpir.ump.edu.my/id/eprint/33320/; https://thesai.org/Downloads/Volume13No1/Paper_92-Keyphrases_Concentrated_Area_Identification_from_Academic_Articles.pdf
• Institution: Universiti Malaysia Pahang

The extraction of high-quality keywords and sum-marising documents at a high level has become more difficult in current research due to technological advancements and the expo-nential expansion of textual data and digital sources. Extracting high-quality keywords and summarising the documents at a high-level need to use features for the keyphrase extraction, becoming more popular. A new unsupervised keyphrase concentrated area (KCA) identification approach is proposed in this study as a feature of keyphrase extraction: corpus, domain and language independent; document length-free; utilized by both supervised and unsupervised techniques. In the proposed system, there are three phases: data pre-processing, data processing, and KCA identification. The system employs various text pre-processing methods before transferring the acquired datasets to the data processing step. The pre-processed data is subsequently used during the data processing step. The statistical approaches, curve plotting, and curve fitting technique are applied in the KCA identification step. The proposed system is then tested and evaluated using benchmark datasets collected from various sources. To demonstrate our proposed approach’s effectiveness, merits, and significance, we compared it with other proposed techniques. The experimental results on eleven (11) datasets show that the proposed approach effectively recognizes the KCA from articles as well as significantly enhances the current keyphrase extraction methods based on various text sizes, languages, and domains.