@unpublished{xu2023inheritsumm,
author = {Xu, Yichong and Xu, Ruochen and Iter, Dan and Liu, Yang and Wang, Shuohang and Zhu, Chenguang and Zeng, Michael},
title = {InheritSumm: A General, Versatile and Compact Summarizer by Distilling from GPT},
year = {2023},
month = {May},
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.},
url = {http://approjects.co.za/?big=en-us/research/publication/inheritsumm-a-general-versatile-and-compact-summarizer-by-distilling-from-gpt/},
}