The conventional summarization model often fails to capture critical
information in meeting transcripts, as meeting corpus usually involves multiple
parties with lengthy conversations and is stuffed with redundant and trivial
content. To tackle this problem, we present SVB, an effective and efficient
framework for meeting summarization that `compress' the redundancy while
preserving important content via three processes: sliding-window dialogue
restoration and \textbf{S}coring, channel-wise importance score
\textbf{V}oting, and relative positional \textbf{B}ucketing. Specifically,
under the self-supervised paradigm, the sliding-window scoring aims to rate the
importance of each token from multiple views. Then these ratings are aggregated
by channel-wise voting. Tokens with high ratings will be regarded as salient
information and labeled as \textit{anchors}. Finally, to tailor the lengthy
input to an acceptable length for the language model, the relative positional
bucketing algorithm is performed to retain the anchors while compressing other
irrelevant contents in different granularities. Without large-scale
pre-training or expert-grade annotating tools, our proposed method outperforms
previous state-of-the-art approaches. A vast amount of evaluations and analyses
are conducted to prove the effectiveness of our method.

本文提出了一种名为 SVB 的会议摘要方法，采用滑动窗口对话恢复和得分、频道重要性得分投票和相对位置分桶算法等三种技术，能够在不需要大规模预训练或专业标注工具的情况下，准确高效地压缩冗余信息并保留重要内容，从而优于现有的会议摘要方法。