Efficient Text Summarization Using PSO-Optimized LSTM-Gated RoBERTa Algorithm for Social Media Content Analysis"

Abstract

Nowadays, social media is an important aspect of news reading, learning, and handling digital information. Increasing detailed information on social media content leads to a vast of time to know the summary of the information. Prevailing techniques and Artificial intelligence handle the text summarization based on the importance of term frequency. Analyzing more feature dependencies like subject, nouns, key terms, and topic modeling makes it more tedious to degrade sentence fragmentation, which leads to low precision and recall rate. To resolve this problem and introduce new sentence-based frequency fragmentation, text summarization is implemented based on PSO optimized LSTM gated RoBERTa Algorithm for social media content extraction. Initially, the text preprocessing is framed to process stop word removal stemming and tokenization to normalize the document data. By reducing the sentence and extracting the important terms by evaluating based on Inverse Term quantum vector frequency evaluation (ITQCF). The sentence term frequency evaluation is carried out using best fit term frequency evaluation using PSO to select the important features. Finally, the optimized auto coders based on the LSTM Gated Robustly Optimized BERT Pretraining Approach (RoBERTa) are used to summarize the content. The Sequence Ranking sentence fragmentation (SRSF) formalizes the sentence depending on the topic to finalize the precision summarization. The proposed system improves the precision rate as well in fragmentation and sentence score to show high performance in F1 score and redundant time complexity.