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

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. Copyright © 2023 Inderscience Enterprises Ltd.