The increased prevalence of online meetings has significantly enhanced the
practicality of a model that can automatically generate the summary of a given
meeting. This paper introduces a novel and effective approach to automate the
generation of meeting summaries. Current approaches to this problem generate
general and basic summaries, considering the meeting simply as a long dialogue.
However, our novel algorithms can generate abstractive meeting summaries that
are driven by the action items contained in the meeting transcript. This is
done by recursively generating summaries and employing our action-item
extraction algorithm for each section of the meeting in parallel. All of these
sectional summaries are then combined and summarized together to create a
coherent and action-item-driven summary. In addition, this paper introduces
three novel methods for dividing up long transcripts into topic-based sections
to improve the time efficiency of our algorithm, as well as to resolve the
issue of large language models (LLMs) forgetting long-term dependencies. Our
pipeline achieved a BERTScore of 64.98 across the AMI corpus, which is an
approximately 4.98% increase from the current state-of-the-art result produced
by a fine-tuned BART (Bidirectional and Auto-Regressive Transformers) model.

本文介绍了一种新颖且有效的自动生成会议摘要的方法，采用递归生成摘要和并行应用行动项提取算法来生成行动项驱动的抽象会议摘要；同时引入了三种划分长篇会议记录成主题部分的方法，以提高算法的时间效率，并解决大语言模型遗忘长期依赖的问题。实验结果在 AMI 语料库上取得了 64.98 的 BERTScore，相对于精调的 BART 模型，提高了约 4.98% 的准确度。