论文标题
一个用于抽象性会议汇总的分层网络，并进行了跨域预处理
A Hierarchical Network for Abstractive Meeting Summarization with Cross-Domain Pretraining
论文作者
论文摘要
借助大量的自动会议笔录，会议汇总对参与者和其他各方都引起了极大的兴趣。汇总会议的传统方法取决于复杂的多步管道，使关节优化棘手。同时，文本摘要和对话系统有一些深层神经模型。但是，会议笔录的语义结构和样式与文章和对话完全不同。在本文中，我们提出了一个新颖的抽象摘要网络，该网络适应了会议方案。我们设计了一个层次结构，以适应长期的会议记录和一个角色矢量来描述说话者之间的差异。此外，由于满足摘要数据的不足，我们将模型预算出大规模新闻摘要数据。经验结果表明，我们的模型在自动指标和人类评估中的表现都优于先前的方法。例如，在ICSI数据集上，Rouge-1得分从34.66％增加到46.28％。
With the abundance of automatic meeting transcripts, meeting summarization is of great interest to both participants and other parties. Traditional methods of summarizing meetings depend on complex multi-step pipelines that make joint optimization intractable. Meanwhile, there are a handful of deep neural models for text summarization and dialogue systems. However, the semantic structure and styles of meeting transcripts are quite different from articles and conversations. In this paper, we propose a novel abstractive summary network that adapts to the meeting scenario. We design a hierarchical structure to accommodate long meeting transcripts and a role vector to depict the difference among speakers. Furthermore, due to the inadequacy of meeting summary data, we pretrain the model on large-scale news summary data. Empirical results show that our model outperforms previous approaches in both automatic metrics and human evaluation. For example, on ICSI dataset, the ROUGE-1 score increases from 34.66% to 46.28%.
