  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.
