Title: Enhancing multiple document summarisation with DNETCNN and BCHOA techniques

Authors: Mamatha Mandava; Surendra Reddy Vinta

Addresses: School of Computer Science and Engineering, VIT-AP University, Amaravathi, India ' School of Computer Science and Engineering, VIT-AP University, Amaravathi, India

Abstract: Multi-document summarising (MDS) is a helpful method for information aggregation that creates a clear and informative summary from a collection of papers linked to the same subject. Due to the significant number of information available online, it might be challenging to extract the needed information from an internet source these days. To generate the summary, we propose the binary chimp optimisation algorithm (BChOA) in this research. Several preprocessing techniques utilised to remove unwanted terms from the content. Then, for word embedding, FastText is used. The semantic and synthetic features are extracted using the DarkNet-53 and ConvNeXt methods. Using a darknet convolutional neural network (DNetCNN), the features derived from the syntactic and semantic features are concatenated. The Movie review dataset contains 2000 review files, and the BBC news dataset has 50 unique documents. Finally, the outcome demonstrates that our model compares to cutting-edge solutions in terms of semantics and syntactic structure.

Keywords: multi-document summarisation; MDS; binary chimp optimisation algorithm; BChOA; ConvNeXt approach; darknet convolutional neural network; DNetCNN.

DOI: 10.1504/IJBIDM.2025.145369

International Journal of Business Intelligence and Data Mining, 2025 Vol.26 No.3/4, pp.257 - 281

Received: 13 Dec 2023
Accepted: 02 Aug 2024
Published online: 31 Mar 2025 *

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