CINOSUM:An Extractive Summarization Model for Low-resource Multi-ethnic Language
To address the issue of existing models being unable to handle abstractive summarization for low-resource multilingual languages,this paper proposes an extractive summarization model,CINOSUM,based on CINO(a Chinese minority pre-trained language model).We construct a multi-ethnic language summarization dataset,MESUM,to extend the linguistic scope of text summarization.To overcome the poor performance of previous models on low-resource languages,a unified sentence extraction framework is employed for extractive summarization across various ethnic languages.In addition,we introduce a joint training strategy for multilingual datasets that effectively expands applications in low-resource languages,thereby greatly improving the model's adaptability and flexibility.Ultimately,this paper conducts extensive experimental study on the MESUM dataset,and the results reveal that the CINOSUM model demonstrates superior performance in multilingual low-resource linguistic environments,including Tibetan and Uyghur languages,achieving significant improvements in the ROUGE evaluation metric.
Extractive summarizationMultilingual pre-trained modelLow-resource language processingKnowledge transfer
NETL
NSTL
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