Hybrid Extractive Abstractive Summarization for Multilingual Sentiment Analysis

• URL: http://arxiv.org/abs/2506.06929v1
• Date: Sat, 07 Jun 2025 21:44:31 GMT
• Title: Hybrid Extractive Abstractive Summarization for Multilingual Sentiment Analysis
• Authors: Mikhail Krasitskii, Grigori Sidorov, Olga Kolesnikova, Liliana Chanona Hernandez, Alexander Gelbukh,
• Abstract summary: The model integrates TF-IDF-based extraction with a fine-tuned XLM-R abstractive module, enhanced by dynamic thresholding and cultural adaptation.<n>Experiments across 10 languages show significant improvements over baselines, achieving 0.90 accuracy for English and 0.84 for low-resource languages.
• Score: 42.90274643419224
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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 by dynamic thresholding and cultural adaptation. Experiments across 10 languages show significant improvements over baselines, achieving 0.90 accuracy for English and 0.84 for low-resource languages. The approach also demonstrates 22% greater computational efficiency than traditional methods. Practical applications include real-time brand monitoring and cross-cultural discourse analysis. Future work will focus on optimization for low-resource languages via 8-bit quantization.

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