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

Abstract

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, P yLDAvis webbased 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. P yLDAvis 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 (ROU GE) metrics to evaluate the reliability and validity of the model.

Hybrid model for extractive single document summarization: utilizing BERTopic and BERT model

“…Several studies have also demonstrated the viability of a topic modeling approach on extractive summarization [14]- [16]. Those studies incorporated bag-of-words approach-based algorithms, such as latent dirichlet allocation (LDA) and latent semantic analysis (LSA).…”

Digital Health Discussion Through Articles Published Until the Year 2021: A Digital Topic Modeling Approach (Preprint)

“…The analyst should find the optimal interpretation by changing lambda to a value between 0 and 1. If lambda is close to 0, the characteristics of the subject are emphasized, but unnecessary junk words may also be extracted; if lambda is close to 1, words that reveal the characteristics of the subject may not appear [29].…”

Cheap, Quick, and Rigorous: Artificial Intelligence and the Systematic Literature Review

“…LDA also facilitates abstraction through topic summarisation. LDA generates a set of word-topic distributions representing the probability of each word occurring in each topic (Onah et al, 2022). Through examining the most probable words that are associated within each topic, researchers can gain an understanding of the main concepts and themes that are represented by the topics.…”

“…Through examining the most probable words that are associated within each topic, researchers can gain an understanding of the main concepts and themes that are represented by the topics. This summarisation aids in distilling key information and abstracting the data (Onah et al, 2022). This then helps to accomplish a key outcome sought throughout an SLR; the identification of gaps in the research domain under investigation through a comprehensive summary of its pertinent research (Paul et al, 2021).…”