Hybrid Extractive Abstractive Summarization for Multilingual Sentiment Analysis
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910994992726016 |
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| author | Krasitskii, Mikhail Sidorov, Grigori Kolesnikova, Olga Hernandez, Liliana Chanona Gelbukh, Alexander |
| author_facet | Krasitskii, Mikhail Sidorov, Grigori Kolesnikova, Olga Hernandez, Liliana Chanona Gelbukh, Alexander |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_06929 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hybrid Extractive Abstractive Summarization for Multilingual Sentiment Analysis Krasitskii, Mikhail Sidorov, Grigori Kolesnikova, Olga Hernandez, Liliana Chanona Gelbukh, Alexander Computation and Language 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. |
| title | Hybrid Extractive Abstractive Summarization for Multilingual Sentiment Analysis |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2506.06929 |