Using Multimodal and Language-Agnostic Sentence Embeddings for Abstractive Summarization
Abstractive summarization aims to generate concise summaries by creating new sentences, allowing for flexible rephrasing. However, this approach can be vulnerable to inaccuracies, particularly `hallucinations' where the model introduces non-existent information. In this paper, we leverage the use of multimodal and multilingual sentence embeddings derived from pretrained models such as LaBSE, SONAR, and BGE-M3, and feed them into a modified BART-based French model. A Named Entity Injection mechanism that appends tokenized named entities to the decoder input is introduced, in order to improve the factual consistency of the generated summary. Our novel framework, SBARThez, is applicable to b...
| Authors | Chaimae Chellaf, Salima Mdhaffar, Yannick Estève et al. |
|---|---|
| Primary category | cs.CL |
| Published | 9 March 2026 |
| Last updated at source | 9 March 2026 |
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