Abstractive speech summarization (SSUM) aims to generate human-like summaries
from speech. Given variations in information captured and phrasing, recordings
can be summarized in multiple ways. Therefore, it is more reasonable to
consider a probabilistic distribution of all potential summaries rather than a
single summary. However, conventional SSUM models are mostly trained and
evaluated with a single ground-truth (GT) human-annotated deterministic summary
for every recording. Generating multiple human references would be ideal to
better represent the distribution statistically, but is impractical because
annotation is expensive. We tackle this challenge by proposing AugSumm, a
method to leverage large language models (LLMs) as a proxy for human annotators
to generate augmented summaries for training and evaluation. First, we explore
prompting strategies to generate synthetic summaries from ChatGPT. We validate
the quality of synthetic summaries using multiple metrics including human
evaluation, where we find that summaries generated using AugSumm are perceived
as more valid to humans. Second, we develop methods to utilize synthetic
summaries in training and evaluation. Experiments on How2 demonstrate that
pre-training on synthetic summaries and fine-tuning on GT summaries improves
ROUGE-L by 1 point on both GT and AugSumm-based test sets. AugSumm summaries
are available at this https URL

通过使用大型语言模型（LLM）作为人工标注者的代理，本研究提出了一种名为 AugSumm 的方法来生成用于训练和评估的增广摘要，通过在 ChatGPT 上生成的合成摘要验证其质量，并在训练和评估中利用这些合成摘要，实验证明在合成摘要上进行预训练并在 GT 摘要上进行微调可以改善 ROUGE-L 指标。