{"type":"ScholarlyArticle","title":"Revealing Semantic Structures of Texts: Multi-grained Framework for Automatic Mind-map Generation","authors":[{"name":"Yang Wei","slug":"yang-wei","given":"Yang","family":"Wei","affiliation":"College of Computer Science , Nankai University"},{"name":"Honglei Guo","slug":"honglei-guo","given":"Honglei","family":"Guo","affiliation":"IBM Research -China weiyang"},{"name":"Jinmao Wei","slug":"jinmao-wei","given":"Jinmao","family":"Wei","affiliation":"College of Computer Science , Nankai University"},{"name":"Zhong Su","slug":"zhong-su","given":"Zhong","family":"Su","affiliation":"IBM Research -China weiyang"}],"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.","doi":"10.24963/ijcai.2019/729","url":"https://ijcai.org/proceedings/2019/729","pdf":"https://ijcai.org/proceedings/2019/0729.pdf","markdown":"https://ijcai.org/proceedings/2019/729.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/729","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Natural Language Processing","track":"Main track","number":729,"pages":{"first":5247,"last":5254},"keywords":["Natural Language Processing: Information Retrieval","Natural Language Processing: Natural Language Summarization","Natural Language Processing: NLP Applications and Tools"],"videos":[],"references":[{"position":1,"raw":"[Brucks and Schommer, 2008] Claudine Brucks and Christoph Schommer. 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