Revealing Semantic Structures of Texts: Multi-grained Framework for Automatic Mind-map Generation

Revealing Semantic Structures of Texts: Multi-grained Framework for Automatic Mind-map Generation

Yang Wei, Honglei Guo, Jinmao Wei, Zhong Su

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
Main track. Pages 5247-5254. https://doi.org/10.24963/ijcai.2019/729

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.
Keywords:
Natural Language Processing: Information Retrieval
Natural Language Processing: Natural Language Summarization
Natural Language Processing: NLP Applications and Tools