Revealing Semantic Structures of Texts: Multi-grained Framework for AutomaticMind-map Generation
Abstract
A mind-map is a diagram used to represent ideas
linked to and arranged around a central concept.
It’s easier to visually access the knowledge and
ideas by converting a text to a mind-map. However,
highlighting the semantic skeleton of an article remains a challenge. The key issue is to detect the
relations amongst concepts beyond intra-sentence.
In this paper, we propose a multi-grained framework for automatic mind-map generation. That is, a
novel neural network is taken to detect the relations
at first, which employs multi-hop self-attention and
gated recurrence network to reveal the directed semantic relations via sentences. A recursive algorithm is then designed to select the most salient sentences to constitute the hierarchy. The human-like
mind-map is automatically constructed with the key
phrases in the salient sentences. Promising results
have been achieved on the comparison with manual
mind-maps. The case studies demonstrate that the
generated mind-maps reveal the underlying semantic structures of the articles