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.

翻译：在线会议的普及显著提升了自动生成会议摘要模型的实用性。本文提出一种新颖且有效的方法来自动生成会议摘要。当前方法通常将会议视为长对话，生成通用且基础的摘要。然而，我们提出的新型算法能够基于会议记录中的行动项生成抽象式摘要。该算法通过递归生成摘要，并并行地对会议每个部分应用行动项提取算法来实现。所有部分摘要随后被合并并再次摘要，以生成连贯且以行动项驱动的最终摘要。此外，本文引入了三种基于主题划分长记录的新方法，以提高算法的时间效率，并解决大型语言模型（LLMs）遗忘长期依赖关系的问题。我们的流水线在AMI语料库上达到了64.98的BERTScore，相比当前由微调后的BART（双向自回归Transformer）模型生成的最优结果提升了约4.98%。