InheritSumm: A General, Versatile and Compact Summarizer by Distilling from GPT

• URL: http://arxiv.org/abs/2305.13083v1
• Date: Mon, 22 May 2023 14:52:32 GMT
• Title: InheritSumm: A General, Versatile and Compact Summarizer by Distilling from GPT
• Authors: Yichong Xu, Ruochen Xu, Dan Iter, Yang Liu, Shuohang Wang, Chenguang Zhu, Michael Zeng
• Abstract summary: InheritSumm is a versatile and compact summarization model derived from GPT-3.5 through distillation. It achieves similar or superior performance to GPT-3.5 in zeroshot and fewshot settings.
• Score: 75.29359361404073
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: While large models such as GPT-3 demonstrate exceptional performance in zeroshot and fewshot summarization tasks, their extensive serving and fine-tuning costs hinder their utilization in various applications. Conversely, previous studies have found that although automatic metrics tend to favor smaller fine-tuned models, the quality of the summaries they generate is inferior to that of larger models like GPT-3 when assessed by human evaluators. To address this issue, we propose InheritSumm, a versatile and compact summarization model derived from GPT-3.5 through distillation. InheritSumm not only exhibits comparable zeroshot and fewshot summarization capabilities to GPT-3.5 but is also sufficiently compact for fine-tuning purposes. Experimental results demonstrate that InheritSumm achieves similar or superior performance to GPT-3.5 in zeroshot and fewshot settings. Furthermore, it outperforms the previously established best small models in both prefix-tuning and full-data fine-tuning scenarios.

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