Title: An effective abstract text summarisation using shark smell optimised bidirectional encoder representations from transformer

Authors: M. Nafees Muneera; P. Sriramya

Addresses: Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamilnadu, 602105, India ' Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, India

Abstract: Recently, a vast amount of text data has increased rapidly and therefore information must be summarised to retrieve useful knowledge. First, the preprocessing module utilises the fixed-length stemming method, and then the segmentation module makes use of a pre-trained bidirectional encoder representations from transformers (BERT). The text of input is segmented with the utilisation of feedforward and multi-head attention layer. This BERT segmentation paradigm is adjoined alongside shark smell optimisation (SSO) methodology, and thus, the phrases that are extricated are employed to prepare the document stage of a dataset of Amazon merchandise assessment. This study aspires to create a concise summary and invigorating headlines, which grab the focus of the readers. This paper demonstrates that it performs by amalgamating the duo extractive and abstractive procedures employing a pipelined technique for creating a succinct summary that is later utilised for headline creation. Experimentation was executed on publicly accessible datasets - CNN/Daily Mail.

Keywords: abstractive; text summarisation; optimisation; transformer; clustering; similarity index.

DOI: 10.1504/IJBIDM.2023.131796

International Journal of Business Intelligence and Data Mining, 2023 Vol.23 No.1, pp.50 - 72

Received: 07 Jun 2021
Accepted: 15 Dec 2021
Published online: 03 Jul 2023 *

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