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Hybrid Extractive Abstractive Summarization for Multilingual Sentiment Analysis

arXiv 2506.06929 ↗First posted 7 Jun 2025cs.CLcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 12 of its 32 references that are in the map. First posted on arXiv 7 Jun 2025.

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References · 12

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  4. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  5. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,935
  6. 2014Sequence to Sequence Learning with Neural Networkscited by 2,986
  7. 2017Get To The Point: Summarization with Pointer-Generator Networkscited by 1,292
  8. 2019Cross-lingual Language Model Pretrainingcited by 1,090
  9. 2016Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyondcited by 812
  10. 2019SemEval-2017 Task 4: Sentiment Analysis in Twittercited by 210
  11. 2024Ethio-Fake: Cutting-Edge Approaches to Combat Fake News in Under-Resourced Languages Using Explainable AIcited by 8
  12. 2025Comparative Approaches to Sentiment Analysis Using Datasets in Major European and Arabic Languagescited by 3

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  3. 2024Abstractive Summarization of Low resourced Nepali language using Multilingual Transformers
  4. 2026Multilingual Sentiment Aware Text Summarization A Reinforcement Learning Approach for Consistency Maintenance
  5. 2025Improving Multilingual Social Media Insights: Aspect-based Comment Analysis
  6. 2025From Annotation to Adaptation: Metrics, Synthetic Data, and Aspect Extraction for Aspect-Based Sentiment Analysis with Large Language Models
  7. 2025Parallel Tokenizers: Rethinking Encoder Models' Vocabulary Design in Cross-Lingual Transfer of Low-Resource Languages
  8. 2026Large-Scale Aspect-Based Sentiment Analysis with Reasoning-Infused LLMs