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This work proposes a novel hybrid approach that combines the strengths of the extreme learning machine (ELM) and the twin support vector machine (TSVM) to address the challenges of robustness and scalability in multi-label classification, particularly in settings where deep learning is not practical due to limited training instances.
This study proposes a novel two-level diagnostic protocol for benchmarking LLM-summarizers based on the stability of the generated summaries and motivates further research towards development of robust, reliable and trustworthy LLM-summarizers.
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