Comparative Analysis of Different Text Summarization Techniques Using Enhanced Tokenization

As a huge amount of data is being generated everyday, text summarization is a must-have technique to obtain the required information concisely. Summaries reduce reading time. When it comes to researching documents, summaries make the job easier. The challenge of creating a short and fluent summary while retaining important information content and overall meaning is known as automatic text summarization. As a huge amount of data is being generated everyday, text summarization is a must-have technique to obtain the required information concisely. It is simple to deal with summarization in the other languages like English, Turkish, Arabic. But due to the diverse and complex nature of the Bangla language, not much has been done on the technique of summarizing the Bangla text. Given the importance of text summarization, this paper focused on the creation of an extraction-based summary approach that works on Bangla text documents. Here we apply different kind of model for generating a summary for a single bangla text document. As compared to other outcomes, our experimental results are outstanding and people who read the summary evaluated them. Further development of these methods will undoubtedly deliver fascinating results. This can also contribute significantly in the effort to build smart machines, which form the basis of industry 4.0.