Reconstruct Before Summarize: An Efficient Two-Step Framework for Condensing and Summarizing Meeting Transcripts
This addresses the challenge of efficient meeting summarization for users dealing with long conversations, though it is incremental as it builds on existing summarization methods.
The paper tackles the problem of summarizing lengthy and redundant meeting transcripts by proposing a two-step framework that first reconstructs transcripts to identify essential content and then uses a novel positional algorithm for summarization, achieving state-of-the-art performance on AMI and ICSI datasets without large-scale pre-training.
Meetings typically involve multiple participants and lengthy conversations, resulting in redundant and trivial content. To overcome these challenges, we propose a two-step framework, Reconstruct before Summarize (RbS), for effective and efficient meeting summarization. RbS first leverages a self-supervised paradigm to annotate essential contents by reconstructing the meeting transcripts. Secondly, we propose a relative positional bucketing (RPB) algorithm to equip (conventional) summarization models to generate the summary. Despite the additional reconstruction process, our proposed RPB significantly compressed the input, leading to faster processing and reduced memory consumption compared to traditional summarization methods. We validate the effectiveness and efficiency of our method through extensive evaluations and analysis. On two meeting summarization datasets, AMI and ICSI, our approach outperforms previous state-of-the-art approaches without relying on large-scale pre-training or expert-grade annotating tools.