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Text mining techniques are used for trend prediction using keyword analysis; however, these processes result in the formation of relevant and irrelevant keywords. Based on the keywords, various clusters are formed, resulting in various topics. Due to the presence of irrelevant keywords, there are chances of the formation of wrong topics. To overcome this problem, this research contributes to developing an algorithm that deals with topic prediction using a noble technique wherein text summarization is inculcated into topic modeling algorithms. This research focuses on implementing text summarizing to generate summaries of published publications by diverse researchers using the Genism library with an extractive text summarization approach and then applying text mining to it to predict trends in various fields. The current approach was compared with existing techniques based on the parameters used in automatic and semi-automatic text mining techniques. [ABSTRACT FROM AUTHOR]

Fractures of the pediatric craniofacial skeleton can be challenging to engage in. The initial injury and subsequent treatment can cause long-term growth disturbances yielding problematic secondary deformities. It is important that clinicians involved in the care of these patients understand the differences between children and adult fracture patterns and understand the potential long-term effects on the growth of the pediatric skeleton and how to manage these problems when they occur. [ABSTRACT FROM AUTHOR]