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

Abstract

The extraction of high-quality keywords and summarising documents at a high level has become more difficult in current research due to technological advancements and the exponential expansion of textual data and digital sources. Extracting high-quality keywords and summarising the documents at a highlevel 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.

A Systematic Literature Review of Keyphrases Extraction Approaches

“…The method relied on defining the candidate phrases on the parse tree, filtering and part of speech tag approach, which helped to control the computational complexity. Badrul et al propose in [35] a keyphrase concentrated area (KCA) as a new feature to extract the keyphrase from applying some statistical operations. The proposed method is multilingual and not related to a specific field.…”

A New Unsupervised Technique to Analyze the Centroid and Frequency of Keyphrases from Academic Articles

“…Hence, the section covers comparable techniques. Based on the training datasets, There are two sorts of key extraction techniques: unsupervised and supervised [2,29]. Both approaches make use of features and feature extraction techniques.…”

“…Online textual content is either semi-structured or unstructured; examples include academic papers, online journals, news sources, and books [1]. Prior to the development of technology, people could only process this large amount of data, which took a long time [2]. Furthermore, it is difficult to accomplish this massive amount of data because of discrepancies between the quantity of information and manual information process skills, leading to the development of automated keyphrase extraction techniques that use computers' comprehensive computational power to replace physical labor [3,4].…”

“…In general, the keyphrase provides a high degree of document characterisation, summary, and description, which is important for a number of Natural Language Processing (NLP) features such as article classification, clustering, as well as categorization [5,6]. "Despite this, they are employed in a broad range of Digital Information Processing applications including Information Retrieval, Digital Content Management, Recommender Systems [7], and Contextual Advertising" [2]. Among other things, it can be used for search engines, media searches, legal and geographic information retrieval, and digital libraries [2,8,9].…”

“…"Despite this, they are employed in a broad range of Digital Information Processing applications including Information Retrieval, Digital Content Management, Recommender Systems [7], and Contextual Advertising" [2]. Among other things, it can be used for search engines, media searches, legal and geographic information retrieval, and digital libraries [2,8,9].…”