A mind-map is a diagram that represents the central concept and key ideas in
a hierarchical way. Converting plain text into a mind-map will reveal its key
semantic structure and be easier to understand. Given a document, the existing
automatic mind-map generation method extracts the relationships of every
sentence pair to generate the directed semantic graph for this document. The
computation complexity increases exponentially with the length of the document.
Moreover, it is difficult to capture the overall semantics. To deal with the
above challenges, we propose an efficient mind-map generation network that
converts a document into a graph via sequence-to-graph. To guarantee a
meaningful mind-map, we design a graph refinement module to adjust the relation
graph in a reinforcement learning manner. Extensive experimental results
demonstrate that the proposed approach is more effective and efficient than the
existing methods. The inference time is reduced by thousands of times compared
with the existing methods. The case studies verify that the generated mind-maps
better reveal the underlying semantic structures of the document.

本文介绍了一种高效的自动制图方法。该方法使用序列图生成技术将一篇文档转换成关系图，并通过强化学习调整图形来获得更有意义的思维导图，实验结果表明该方法比现有方法更加有效和高效。

通过序列到图和强化图优化生成高效思维导图

Efficient Mind-Map Generation via Sequence-to-Graph and Reinforced Graph  Refinement

We propose an attention-based model that treats AMR parsing as
sequence-to-graph transduction. Unlike most AMR parsers that rely on
pre-trained aligners, external semantic resources, or data augmentation, our
proposed parser is aligner-free, and it can be effectively trained with limited
amounts of labeled AMR data. Our experimental results outperform all previously
reported SMATCH scores, on both AMR 2.0 (76.3% F1 on LDC2017T10) and AMR 1.0
(70.2% F1 on LDC2014T12).

我们提出了一种基于关注机制的模型，将 AMR 解析视为序列到图的转换，与大多数依赖于预训练对齐程序、外部语义资源或数据增强的 AMR 解析器不同，我们的提议的解析器是免费的，它可以有效地训练用于有限数量的标记 AMR 数据。我们的实验结果优于以前报告的所有 SMATCH 得分，无论是在 AMR 2.0（LDC2017T10 上的 76.3％F1）还是 AMR 1.0（LDC2014T12 上的 70.2％F1）上。