Efficient Mind-Map Generation via Sequence-to-Graph and Reinforced Graph Refinement (2021)

Abstract: 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.

## introduction a mind-map is a hierarchical diagram that can depict the central concept, linked with the major ideas and other ideas branch out from these @xcite . it is organized in cognitive structures and much easier to understand than plain text @xcite . thus in practice, it can be utilized for education resources, organizing, and planning. many tools can help people make mind-map manually, such as freemind, mindgenius and visual mind, etc @xcite . to save human labors, some automatic methods have been proposed to generate mind-map from text, which focus on analyzing the semantic relations * honglei guo is the corresponding author. ## related works a mind-map is a hierarchical diagram to reveal the semantic structure for a document. the salientsentence-based mind-map (ssm) is similar with extractive summarization @xcite , which aims to choose some key sentences from the document to describe the main ideas. similar but completely different, mind-map generation can reveal not only the main ideas, but also the key semantic logic structure of the document. one previous work lexrank @xcite computes an adjacency matrix of the graph representation of sentences based on intra-sentence cosine similarity. however, the lexical similarity of some sentence pairs with semantic relation may be zero. generating a mind-map from this graph representation tends to be less-meaningful, which is also indicated in the experiments (see §3.4). in addition, a few extractive summarization works employ graph techniques. for instance, a bipartite graph for sentence and entity nodes @xcite or a weighted graph with topic nodes @xcite are proposed to improve ranking the sentences in a document. recently, @xcite propose to build a heterogeneous graph to learn the correlation between word and sentence nodes, which helps to select better-summarizing sentences. though these works involve learning the graph knowledge, such graphs are hard to derive a mind-map that focuses on the governing relationships between sentences. another related direction is the policy-based reinforcement learning @xcite . previous methods @xcite usually affect the training of the main task by a policy network with separate parameters. different from them, we directly regard the main network as the policy network and its output graph as the action distribution. then the main network is optimized simultaneously when maximizing the expected reward. ## conclusion we propose an efficient mind-map generation network that converts a document into a graph via sequence-to-graph. to ensure a meaningful mindmap, we design a graph refinement module to adjust the graph by leveraging the highlights 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 approaches. the case studies further verify that the generated mind-map can reveal the underlying semantic structures of a document.