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With the advent and popularity of big data mining and huge text analysis inmodern times, automated text summarization became prominent for extracting andretrieving important information from documents. This research investigatesaspects of automatic text summarization from the perspectives of single andmultiple documents. Summarization is a task of condensing huge text articlesinto short, summarized versions. The text is reduced in size for summarizationpurpose but preserving key vital information and retaining the meaning of theoriginal document. This study presents the Latent Dirichlet Allocation (LDA)approach used to perform topic modelling from summarised medical sciencejournal articles with topics related to genes and diseases. In this study,PyLDAvis web-based interactive visualization tool was used to visualise theselected topics. The visualisation provides an overarching view of the maintopics while allowing and attributing deep meaning to the prevalence individualtopic. This study presents a novel approach to summarization of single andmultiple documents. The results suggest the terms ranked purely by consideringtheir probability of the topic prevalence within the processed document usingextractive summarization technique. PyLDAvis visualization describes theflexibility of exploring the terms of the topics' association to the fitted LDAmodel. The topic modelling result shows prevalence within topics 1 and 2. Thisassociation reveals that there is similarity between the terms in topic 1 and 2in this study. The efficacy of the LDA and the extractive summarization methodswere measured using Latent Semantic Analysis (LSA) and Recall-OrientedUnderstudy for Gisting Evaluation (ROUGE) metrics to evaluate the reliabilityand validity of the model.

A Data-driven Latent Semantic Analysis for Automatic Text Summarization using LDA Topic Modelling