Extractive Summarization Of Bible Chapters Using Topic Modeling

Abstract

Text mining is a relatively new field that is gradually unrolling into a variety of disciplines. It is gaining more and more popular as technology continues to advance and there is a greater demand for effective text analysis. This text mining is used to make predictions with a particular level of certainty while only evaluating a small portion of the text. The goal of text mining itself is not to establish strict rules by analyzing the entire data set. However, the challenge stems from the ability to convey the same information in a variety of ways through the use of language. Research on analytics has a long tradition of obtaining information from the data. In recent years, it had played an important role and gained prominence and influence because there will be a greater need to access the summary of data within the smallest amount of time as the world moves further into the digital age, generating vast amounts of data and born to a huge amount of digital content. This is because all of us want to know the thing or information in summarized form and of course the information that is most of paramount importance. Accessing pertinent content summary not only helps in the drive to learn, but it is also a need to design tools that break down the accessibility of key relevant summaries. Thus, obtaining reliable information from a large amount of data has become an increasingly difficult problem in Andhra University, Visakhapatnam searching and summarizing to find the hidden semantic structure encoded in the literature that is available electronically. In recent years, it has become recognized as a solution to the problem of retrieving useful information from massive texts through the approach of text summarization. Moreover, the growth and expansion of this concept called for a careful investigation carried out by the interpretation of various forms of soft computing techniques. newline newline