Efficient text summarization method for blind people using text mining techniques
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Efficient text summarization method for blind people using text mining techniques Shakila Basheer1 · M. Anbarasi2 · Darpan Garg Sakshi2 · V. Vinoth Kumar3 Received: 8 December 2019 / Accepted: 27 April 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Owing to the phenomenal growth in communication technology, most of us hardly have time to read books. This habit of reading is slowly diminishing because of the busy lives of people. For visually challenged people, the situation is even worse. In order to address this impedes, we develop a better and more accurate methodology than the existing ones. In this work, in order to save the efforts for reading the complete text every time, we modify the Weighted TF_IDF (Term Frequency Inverse Document Frequency) algorithm to summarize books into relevant keywords. Then, we compare the modified algorithm with that of the existing algorithms of TextRank Algorithm, Luhn’s Algorithm, LexRank Algorithm, Latent Semantic Analysis(LSA). From the comparative analysis, we find that Weighted TF_IDF is an efficient algorithm to automate text summarization and produce an effective summary which is then converted from text to speech. Thus, the proposed algorithm would highly be useful for blind people. Keywords Summarizer · Text · Text ranking algorithm · Text-to-speech
1 Introduction Text summarization basically is a method of distilling from a document (or source). After refining the document, the most important information is to create a shortened version for a specific user. Human beings are usually quite good at this position as we can understand a text document’s nature and extract excellent features and then use our language to describe the documents. However, automatic text summary methods are crucial in today’s world because of the
* M. Anbarasi [email protected] Shakila Basheer [email protected] Darpan Garg Sakshi [email protected] V. Vinoth Kumar [email protected] 1
Information System Department, College of Computer and Information Sciences, Princess Norah Bint Abdulrahman University, Riyadh, Saudi Arabia
2
Vellore Institute of Technology, Vellore, Tamil Nadu, India
3
MVJ College of Engineering, Bangalore, India
availability of excess of data, lack of resources and time to interpret the data (Aliguliyev 2007). The automated review of text is the task of creating a concise and articulate explanation while maintaining the material’s key content and context. Several approaches to automated text summarization have been developed in recent years and widely applied in different domains (Yeh et al. 2005). Search engines, for example, create excerpts as document previews. Certain examples include news channels that produce concise news subject details, typically as headlines to encourage searching. Automatic text summarization is very difficult because we commonly read it completely to improve our comprehension and then write a description that highlights the main points when we summarize a piece of text (Aone et al.
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