Automatic text summarization framework for multi-text and multilingual documents using an ensemble of HIN-MELM-AE and improved DePori model
Automatic text summarization (ATS) has gained increasing significance in recent years due to the rapid growth of textual data across digital platforms. The main objective of ATS is to generate a concise, informative summary from a lengthy document. Multi-document and multilingual summarization has been largely underexplored in previous research. This study presents an improved ensemble learning-based ATS system with slang filtering, using the Hyperfan-IN multilayer extreme learning machine-based autoencoder (HIN-MELM-AE) and the improved Dehghani poor-and-rich optimization algorithm (DePori). The original text undergoes comprehensive preprocessing, after which slang is detected and removed using DePori. Subsequently, the clean text is processed through info-squared C-means clustering, latent Dirichlet allocation-based topic modeling, term frequency–inverse document frequency weighting, and frequent-term extraction. Next, part-of-speech (POS) tagging is performed using a sememe similarity-induced hidden Markov model, and key entities are extracted from the transformed and POS–tagged data. Distilled bidirectional encoder representations from transformers (DBERT) are used to convert these entities into vectors. The final summary is generated through a combination of HIN-MELM-AE, stack autoencoder, variational autoencoder, and DBERT models, followed by cosine similarity calculation, voting-based fusion, re-ranking, and selection of the optimal sentences. Experimental results indicate that the proposed framework achieves superior performance 97.92% of the time, outperforming existing ATS methods.
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