Nowadays, the explosive growth of academic literature has been going far beyond scientists' limited capability to read through, making it increasingly difficult for them to absorb disciplinary insights and extract intellectual essences critical for generating novel research ideas in interdisciplinary studies. To address this, we develop DeepReport, an AI-assisted scientific idea generation system to alleviate the research burden. Technically, DeepReport maintains evolving concept co-occurrence graphs to extract core insights from over 260 million publications across all disciplines. These concepts are periodically collected and updated, enabling the automatic extraction of hidden cross-domain connections. Combining temporal link prediction and analysis techniques with large language models, DeepReport is able to further transform these patterns of insights into actionable ideas. With the function of integrating up-to-date academic databases, visualizing dynamic relationships of concepts, and automatically generating new ideas, DeepReport empowers researchers to navigate complex knowledge landscapes, reduce cognitive burdens, and accelerate the generation of groundbreaking concepts. This work provides an in-depth exploration of DeepReport's architecture, functionalities, and applications, highlighting its transformative potential for advancing interdisciplinary research and fostering innovation. DeepReport is available at https://idea.acemap.cn/.

Shuqian Sheng, Yi Xu, Tianhang Zhang, Zanwei Shen, Luoyi Fu, Jiaxin Ding, Lei Zhou, Xiaoying Gan, Xinbing Wang, Chenghu Zhou. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. 2024.

Shuqian Sheng, Yi Xu, Luoyi Fu, Jiaxin Ding, Lei Zhou, Xinbing Wang, Chenghu Zhou. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.

Yi Xu, Shuqian Sheng, Jiexing Qi, Luoyi Fu, Zhouhan Lin, Xinbing Wang, Chenghu Zhou. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.

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.1 * Luoyi Fu is the corresponding author. 1 The project is publicly available for research purpose https://github.com/xyjigsaw/Kiscovery.