{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T11:10:37Z","timestamp":1780053037482,"version":"3.54.0"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,8]]},"abstract":"<jats:p>A mind-map is a diagram used to represent ideas linked to and arranged around a central concept. It\u2019s 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.<\/jats:p>","DOI":"10.24963\/ijcai.2019\/729","type":"proceedings-article","created":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T07:46:05Z","timestamp":1564299965000},"page":"5247-5254","source":"Crossref","is-referenced-by-count":3,"title":["Revealing Semantic Structures of Texts: Multi-grained Framework for Automatic Mind-map Generation"],"prefix":"10.24963","author":[{"given":"Yang","family":"Wei","sequence":"first","affiliation":[{"name":"College of Computer Science, Nankai University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Honglei","family":"Guo","sequence":"additional","affiliation":[{"name":"IBM Research - China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinmao","family":"Wei","sequence":"additional","affiliation":[{"name":"College of Computer Science, Nankai University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhong","family":"Su","sequence":"additional","affiliation":[{"name":"IBM Research - China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"10584","event":{"name":"Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}","theme":"Artificial Intelligence","location":"Macao, China","acronym":"IJCAI-2019","number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2019,8,10]]},"end":{"date-parts":[[2019,8,16]]}},"container-title":["Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T07:51:25Z","timestamp":1564300285000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2019\/729"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2019,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2019\/729","relation":{},"subject":[],"published":{"date-parts":[[2019,8]]}}}