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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 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.
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Keep Meeting Summaries on Topic: Abstractive Multi-Modal Meeting Summarization
Transcripts of natural, multi-person meetings differ significantly from documents like news articles, which can make Natural Language Generation models generate unfocused summaries. We develop an abstractive meeting summarizer from both videos and audios of meeting recordings. Specifically, we propose a multi-modal hierarchical attention mechanism across three levels: topic segment, utterance and word. To narrow down the focus into topically-relevant segments, we jointly model topic segmentation and summarization. In addition to traditional textual features, we introduce new multi-modal features derived from visual focus of attention, based on the assumption that an utterance is more important if its speaker receives more attention. Experiments show that our model significantly outperforms the state-of-the-art with both BLEU and ROUGE measures.
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- Award ID(s):
- 1631674
- PAR ID:
- 10107386
- Date Published:
- Journal Name:
- 57th Conference of the Association for Computational Linguistics
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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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.more » « less
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