Exploring and Verbalizing Academic Ideas by Concept Co-occurrence
Yi Xu Shuqian Sheng Bo Xue Luoyi Fu Xinbing Wang Chenghu Zhou
Researchers usually come up with new ideas only after thoroughly
comprehending vast quantities of literature. The difficulty of this procedure
is exacerbated by the fact that the number of academic publications is growing
exponentially. In this study, we devise a framework based on concept
co-occurrence for academic idea inspiration, which has been integrated into a
research assistant system. From our perspective, the fusion of two concepts
that co-occur in an academic paper can be regarded as an important way of the
emergence of a new idea. We construct evolving concept graphs according to the
co-occurrence relationship of concepts from 20 disciplines or topics. Then we
design a temporal link prediction method based on masked language model to
explore potential connections between different concepts. To verbalize the
newly discovered connections, we also utilize the pretrained language model to
generate a description of an idea based on a new data structure called
co-occurrence citation quintuple. We evaluate our proposed system using both
automatic metrics and human assessment. The results demonstrate that our system
has broad prospects and can assist researchers in expediting the process of
discovering new ideas.