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Summary sentences produced by abstractive summarization models may becoherent and comprehensive, but they lack control and rely heavily on referencesummaries. The BRIO training paradigm assumes a non-deterministic distributionto reduce the model's dependence on reference summaries, and improve modelperformance during inference. This paper presents a straightforward buteffective technique to improve abstractive summaries by fine-tuning pre-trainedlanguage models, and training them with the BRIO paradigm. We build a textsummarization dataset for Vietnamese, called VieSum. We perform experimentswith abstractive summarization models trained with the BRIO paradigm on theCNNDM and the VieSum datasets. The results show that the models, trained onbasic hardware, outperform all existing abstractive summarization models,especially for Vietnamese.

Abstractive Text Summarization Using the BRIO Training Paradigm