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

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