Hybrid Extractive Abstractive Summarization for Multilingual Sentiment Analysis

Abstract

We propose a hybrid approach for multilingual sentiment analysis that combines extractive and abstractive summarization to address the limitations of standalone methods. The model integrates TF-IDF-based extraction with a fine-tuned XLM-R abstractive module, enhanced through dynamic thresholding and cultural adaptation. Experiments across 10 languages demonstrate significant improvements over baselines, achieving an accuracy of 0.90 for English and 0.84 for low-resource languages. The approach also achieves 22% greater computational efficiency compared to traditional methods. Practical applications include real-time brand monitoring and cross-cultural discourse analysis. Future work will focus on optimizing performance for low-resource languages through 8-bit quantization.