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

For human beings, the processing of text streams of unknown size leads
generally to problems because e.g. noise must be selected out, information be
tested for its relevance or redundancy, and linguistic phenomenon like
ambiguity or the resolution of pronouns be advanced. Putting this into
simulation by using an artificial mind-map is a challenge, which offers the
gate for a wide field of applications like automatic text summarization or
punctual retrieval. In this work we present a framework that is a first step
towards an automatic intellect. It aims at assembling a mind-map based on
incoming text streams and on a subject-verb-object strategy, having the verb as
an interconnection between the adjacent nouns. The mind-map's performance is
enriched by a pronoun resolution engine that bases on the work of D. Klein, and
C. D. Manning.

本文介绍了一种构建人工智能心智图的框架，通过主谓宾策略，并结合 D. Klein 和 C. D. Manning 的代词消解引擎，可用于自动文本摘要和特定信息的检索。