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Online information has increased tremendously in today’s age of the Internet. As a result, the need has arisen to extract relevant content from the plethora of available information. Researchers are widely using automatic text summarization techniques for extracting useful and relevant information from voluminous available information. The summary obtained from the automatic text summarization often faces the issues of diversity and information coverage. Earlier researchers have used graph-based approaches for ranking and optimization. This research work introduces a probabilistic approach named as ClusRank for summary extraction, comprising of a two-stage sentence selection model involving clustering and then ranking of sentences. The initial stage involves clustering of sentences using a proposed overlapping clustering algorithm on the weighted network, and later selection of salient sentences using the introduced probabilistic approach. In the analysis of real-world networks, community structure development is essential because it provides strategic insights that help decision-makers make well-informed choices. Furthermore, methodologically strict community detection algorithms are required due to the occurrence of discontinuous, overlapping, and nested community patterns in such networks.This research work, an algorithm is presented for detecting overlapping communities based on the concept of rough set and granular information on links. The sentence selection algorithm based on budget maximum coverage approach supports the assumption that larger sub-topics in a document are of more importance than smaller subtopics. The performance of the proposed probabilistic ClusRank is validated on DUC2001, DUC 2002, DUC2004, and DUC 2006 data sets.

This generation faces existential threats because of the global assault of the novel Corona virus 2019 (i.e., COVID-19). With more than thirteen million infected and nearly 600000 fatalities in 188 countries/regions, COVID-19 is the worst calamity since the World War II. These misfortunes are traced to various reasons, including late detection of latent or asymptomatic carriers, migration, and inadequate isolation of infected people. This makes detection, containment, and mitigation global priorities to contain exposure via quarantine, lockdowns, work/stay at home, and social distancing that are focused on “flattening the curve”. While medical and healthcare givers are at the frontline in the battle against COVID-19, it is a crusade for all of humanity. Meanwhile, machine and deep learning models have been revolutionary across numerous domains and applications whose potency have been exploited to birth numerous state-of-the-art technologies utilised in disease detection, diagnoses, and treatment. Despite these potentials, machine and, particularly, deep learning models are data sensitive, because their effectiveness depends on availability and reliability of data. The unavailability of such data hinders efforts of engineers and computer scientists to fully contribute to the ongoing assault against COVID-19. Faced with a calamity on one side and absence of reliable data on the other, this study presents two data-augmentation models to enhance learnability of the Convolutional Neural Network (CNN) and the Convolutional Long Short-Term Memory (ConvLSTM)-based deep learning models (DADLMs) and, by doing so, boost the accuracy of COVID-19 detection. Experimental results reveal improvement in terms of accuracy of detection, logarithmic loss, and testing time relative to DLMs devoid of such data augmentation. Furthermore, average increases of 4% to 11% in COVID-19 detection accuracy are reported in favour of the proposed data-augmented deep learning models relative to the machine learning techniques. Therefore, the proposed algorithm is effective in performing a rapid and consistent Corona virus diagnosis that is primarily aimed at assisting clinicians in making accurate identification of the virus.