Efficient text summarization method for blind people using text mining techniques

• Published in: International journal of speech technology Vol. 23; no. 4; pp. 713 - 725
• Main Authors: Basheer, Shakila, Anbarasi, M., Sakshi, Darpan Garg, Vinoth Kumar, V.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01-12-2020
• Subjects: Artificial Intelligence; Engineering; Signal,Image and Speech Processing; Social Sciences; Summarizer; Text; Text ranking algorithm; Text-to-speech
• ISSN: 1381-2416; 1572-8110
• DOI: 10.1007/s10772-020-09712-z

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.