  With the advent and popularity of big data mining and huge text analysis in
modern times, automated text summarization became prominent for extracting and
retrieving important information from documents. This research investigates
aspects of automatic text summarization from the perspectives of single and
multiple documents. Summarization is a task of condensing huge text articles
into short, summarized versions. The text is reduced in size for summarization
purpose but preserving key vital information and retaining the meaning of the
original document. This study presents the Latent Dirichlet Allocation (LDA)
approach used to perform topic modelling from summarised medical science
journal articles with topics related to genes and diseases. In this study,
PyLDAvis web-based interactive visualization tool was used to visualise the
selected topics. The visualisation provides an overarching view of the main
topics while allowing and attributing deep meaning to the prevalence individual
topic. This study presents a novel approach to summarization of single and
multiple documents. The results suggest the terms ranked purely by considering
their probability of the topic prevalence within the processed document using
extractive summarization technique. PyLDAvis visualization describes the
flexibility of exploring the terms of the topics' association to the fitted LDA
model. The topic modelling result shows prevalence within topics 1 and 2. This
association reveals that there is similarity between the terms in topic 1 and 2
in this study. The efficacy of the LDA and the extractive summarization methods
were measured using Latent Semantic Analysis (LSA) and Recall-Oriented
Understudy for Gisting Evaluation (ROUGE) metrics to evaluate the reliability
and validity of the model.

Information Retrieval;Machine Learning