Sentiment analysis in multilingual contexts is significantly influenced by text summarization, with the effects varying across languages of different morphological complexities. This study examines the impact of extractive and abstractive summarization techniques on sentiment classification in eight typologically diverse languages: English (analytic), German (fusional), French, Spanish, Italian (moderately synthetic), Finnish and Hungarian (agglutinative), and Arabic (root-based inflectional). Multilingual transformer models (Multilingual Bidirectional Encoder Representations from Transformers (mBERT), Cross-lingual Language Model-Robustly optimized BERT approach (XLM-RoBERTa), T5, Bidirectional and Auto-Regressive Transformer (BART)) and language-specific adaptations (FinBERT, AraBERT) were employed to evaluate performance. It was found that extractive summarization better preserves sentiment accuracy, with modest declines of 2.6% in English (from 92.1% to 88.5%) and 5.2% in German compared to baseline results. In contrast, morphologically complex languages such as Finnish and Arabic exhibited more substantial drops of 8–12%. Abstractive summarization, while enhancing readability (ROUGE-1: 87.1% for English vs 89.3% for extractive methods), introduced significant sentiment distortion in agglutinative and inflectional languages due to semantic drift, including the omission of critical markers like Finnish case endings. A hybrid approach, combining extractive sentiment preservation through Term Frequency-Inverse Document Frequency (TF-IDF) keyphrase retention with abstractive fluency, was proposed and demonstrated superior performance. This method achieved a 4.2% higher F1-score compared to pure abstractive summarization in Arabic. The findings underscore the necessity of language-specific adaptations, particularly for applications such as social media monitoring and multilingual natural language processing (NLP) systems, where morphological complexity directly affects sentiment preservation accuracy.
Krasitskii et al. (Fri,) studied this question.