Title: Multi-document text summarisation using DL-BILSTM model with hybrid algorithms

Authors: Jyotirmayee Rautaray; Sangram Panigrahi; Ajit Kumar Nayak

Addresses: Department of Computer Science and Engineering, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, 751030, India ' Department of Computer Science and Information Technology, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, 751030, India ' Department of Computer Science and Information Technology, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, 751030, India

Abstract: With the overwhelming amount of information available online, it becomes challenging for users to access relevant data. Automated techniques are essential to effectively filter and extract valuable information from vast datasets. Recently, text summarisation has emerged as a key method for distilling relevant content from lengthy documents. This work introduces a novel deep learning-based approach for multi-document text summarisation. The proposed system begins with preprocessing tasks such as stop word removal, sentence and paragraph chunking, stemming, and lemmatisation. Textual phrases are transformed into vector space models using TF-ISF and sentence scores are evaluated. A deep learning-based bidirectional long short-term memory model is employed for summarisation. Additionally, cat swarm optimisation and aquila optimisers refine DL model's parameters. The approach is validated using DUC 2002, DUC 2003, and DUC 2005 datasets, demonstrating superior performance across various metrics including Rouge scores, BLEU scores, cohesion, sensitivity, positive predictive value, and readability when compared to other summarisation methods.

Keywords: multi-document text summarisation; MDTS; BiLSTM; term frequency-inverse sentence frequency; deep learning; Aquila optimiser; cat swarm optimisation; CSO; natural language processing; NLP.

DOI: 10.1504/IJDMMM.2025.148836

International Journal of Data Mining, Modelling and Management, 2025 Vol.17 No.3, pp.334 - 363

Received: 06 Jan 2024
Accepted: 26 Feb 2024
Published online: 29 Sep 2025 *

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