Finalizing your reference list with machine learning

Abstract

A high quality reference list is important to the overall quality of a research paper. However, it requires domain knowledge and is time consuming to generate a reference list with good coverage, representativeness, and timeliness due to the large amount and fast growing of publications. In this paper, we deal with the specific problem of reference enhancement of research manuscripts with machine learning. A predictive model is trained by a large academic dataset with paper-related and venue-related information to discover additional references for a scientific draft with related information including an initial reference list. We propose a supervised approach called RefCom under the framework of learning-to-rank to predict the probability for a given paper to cite a reference candidate. Forty features in total are defined to describe pairs of papers with respect to author influence, venue influence and paper influence, as well as content and reference similarity. Unlike heuristic rule-based approaches, RefCom is able to integrate multiple features with learned weights. Experimental study with the AMiner dataset which contains 2 million papers and 1.7 million authors show the effectiveness of RefCom in citation prediction, suggesting its potential of being applied as an assistant tool in reference finalization.