Cross-Lingual Semantic Alignment and Multi-Objective Reranking for Multilingual Abstractive Summarization
Knowledge-Based Systems. Vol. 351, article 116785. Elsevier, 2026.
Multilingual summarisers often write fluent summaries that drift from what the article says. We fine-tune mT5-Large on XL-Sum in five languages with an alignment loss that ties each article to its translation, then rerank candidate summaries by factual entailment (mDeBERTa-v3) and semantic centrality (MPNet).
- Cross-lingual semantic consistency improved for every language pair, most strongly for English and Swahili (+0.16).
- Expert human ratings rose from 3.42 to 4.13 out of 5.
- Languages: English, French, Spanish, Turkish and Swahili.
Cite: Singla, P., Singh, J., & Chhabra, G. S. (2026). Cross-Lingual Semantic Alignment and Multi-Objective Reranking for Multilingual Abstractive Summarization. Knowledge-Based Systems, vol. 351, article 116785, 2026. https://doi.org/10.1016/j.knosys.2026.116785