Effective CAD research in the sea of papers

In the past decade, there has been a rapid growth in the number of journal, conference and workshop publications from academic research. The growth seems to be accelerated as time goes by. Accordingly, it has become increasingly difficult for researchers to efficiently identify papers related to a given topic, leading to missing important references or even repetitive work. Moreover, even when these papers are found, it is very time-consuming to find their inherent relations. In this paper, using CAD research as a vehicle, we will demonstrate a novel deep learning based framework that can automatically search for papers related to a given abstract of research, and suggest how they are correlated. We also provide the analysis and comparison among several classic machine-learning approaches. Experimental results show that the proposed approach always outperforms the conventional keyword-based rankings, in both accuracy and F1 scores.