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The increased prevalence of online meetings has significantly en- hanced 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 sum- maries. Current approaches to this problem generate general and basic summaries, considering the meeting simply as a long dia- logue. However, our novel algorithms can generate abstractive meet- ing summaries that are driven by the action items contained in the meeting transcript. This is done by recursively generating sum- maries and employing our action-item extraction algorithm for each section of the meeting in parallel. All of these sectional sum- maries 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 al- gorithm, 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 approxi- mately 4.98% increase from the current state-of-the-art result pro- duced by a fine-tuned BART (Bidirectional and Auto-Regressive Transformers) model. 

The increased prevalence of online meetings has significantly en- hanced 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 sum- maries. 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 meet- ing transcript. This is done by recursively generating summaries and employing our action-item extraction algorithm for each sec- tion 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) forget- ting 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.