Journal of Data Analysis and Information Processing

Volume 10, Issue 1 (February 2022)

ISSN Print: 2327-7211   ISSN Online: 2327-7203

Google-based Impact Factor: 1.59  Citations  

Construction of an Automatic Bengali Text Summarizer Using Machine Learning Approaches

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DOI: 10.4236/jdaip.2022.101003    192 Downloads   1,341 Views  Citations

ABSTRACT

In our study, we chose python as the programming platform for finding an Automatic Bengali Document Summarizer. English has sufficient tools to process and receive summarized records. However, there is no specifically applicable to Bengali since Bengali has a lot of ambiguity, it differs from English in terms of grammar. Afterward, this language holds an important place because this language is spoken by 26 core people all over the world. As a result, it has taken a new method to summarize Bengali documents. The proposed system has been designed by using the following stages: pre-processing the sample doc/input doc, word tagging, pronoun replacement, sentence ranking, as well as summary. Pronoun replacement has been used to reduce the incidence of swinging pronouns in the performance review. We ranked sentences based on sentence frequency, numerical figures, and pronoun replacement. Checking the similarity between two sentences in order to exclude one since it has less duplication. Hereby, we’ve taken 3000 data as input from newspaper and book documents and learned the words to be appropriate with syntax. In addition, to evaluate the performance of the designed summarizer, the design system looked at the different documents. According to the assessment method, the recall, precision, and F-score were 0.70, 0.82 and 0.74, respectively, representing 70%, 82% and 74% recall, precision, and F-score. It has been found that the proper pronoun replacement was 72%.

Share and Cite:

Jahan, B. , Khatun, M. , Zabu, Z. , Hoque, A. and Rayhan, S. (2022) Construction of an Automatic Bengali Text Summarizer Using Machine Learning Approaches. Journal of Data Analysis and Information Processing, 10, 43-57. doi: 10.4236/jdaip.2022.101003.

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