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An adaptive search mechanism with convolutional learning networks for online social media text summarization and
Reema G Al-Anazi1, Muhammad Swaileh A Alzaidi2, Majdy M Eltahir3
1Department of Arabic Language and Literature, College of Humanities and Social Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia.
Abstract:
The fast development of social media platforms has led to an unprecedented growth of daily short text content. Removing valued patterns and insights from this vast amount of textual data requires advanced methods to provide information while preserving its essential components successfully. A text summarization system takes more than one document as input and tries to give a fluent and concise summary of the most significant information in the input. Recent solutions for condensing and reading text are ineffective and time-consuming, provided plenty of information is available online. Concerning this challenge, automated text summarization methods have developed as a convincing choice, achieving important significance in their growth. It was separated into two kinds according to the abstraction methods utilized: abstractive summarization (AS) and extractive summarization (ES). Furthermore, automatic text summarization has many applications and spheres of impact. This manuscript proposes an Adaptive Search Mechanism Based Hierarchical Learning Networks for Social Media Data Summarization and Classification Model (ASMHLN-SMDSCM) technique. The ASMHLN-SMDSCM approach aims to present a novel approach for text summarization on social media using advanced deep learning models. To accomplish that, the proposed ASMHLN-SMDSCM model performs text pre-processing, which contains dissimilar levels employed to handle unprocessed data. The BERT model is used for the feature extraction process. Furthermore, the moth search algorithm (MSA)-based hyperparameter selection process is performed to optimize the feature extraction results of the BERT model. Finally, the classification uses the TabNet and convolutional neural network (TabNet + CNN) model. The efficiency of the ASMHLN-SMDSCM method is validated by comprehensive studies using the FIFA and FARMER datasets. The experimental validation of the ASMHLN-SMDSCM method illustrated a superior accuracy value of 98.87% and 98.55% over recent techniques.
