Control Flow Operators in PyTorch
Yidi Wu, Thomas Bohnstingl, et al.
ICML 2025
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.
Yidi Wu, Thomas Bohnstingl, et al.
ICML 2025
Gosia Lazuka, Andreea Simona Anghel, et al.
SC 2024
Robert Farrell, Rajarshi Das, et al.
AAAI-SS 2010
Ben Fei, Jinbai Liu
IEEE Transactions on Neural Networks