Ayesha Khaliq1, Salman Afsar Awan1, Fahad Ahmad2,*, Muhammad Azam Zia1, Muhammad Zafar Iqbal3
CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3221-3242, 2024, DOI:10.32604/cmc.2024.053488
Abstract The rapid expansion of online content and big data has precipitated an urgent need for efficient summarization techniques to swiftly comprehend vast textual documents without compromising their original integrity. Current approaches in Extractive Text Summarization (ETS) leverage the modeling of inter-sentence relationships, a task of paramount importance in producing coherent summaries. This study introduces an innovative model that integrates Graph Attention Networks (GATs) with Transformer-based Bidirectional Encoder Representations from Transformers (BERT) and Latent Dirichlet Allocation (LDA), further enhanced by Term Frequency-Inverse Document Frequency (TF-IDF) values, to improve sentence selection by capturing comprehensive topical information. Our… More >
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