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In the process of Systematic Literature Review, citation screening isestimated to be one of the most time-consuming steps. Multiple approaches toautomate it using various machine learning techniques have been proposed. Thefirst research papers that apply deep neural networks to this problem werepublished in the last two years. In this work, we conduct a replicability studyof the first two deep learning papers for citation screening and evaluate theirperformance on 23 publicly available datasets. While we succeeded inreplicating the results of one of the papers, we were unable to replicate theresults of the other. We summarise the challenges involved in the replication,including difficulties in obtaining the datasets to match the experimentalsetup of the original papers and problems with executing the original sourcecode. Motivated by this experience, we subsequently present a simpler modelbased on averaging word embeddings that outperforms one of the models on 18 outof 23 datasets and is, on average, 72 times faster than the second replicatedapproach. Finally, we measure the training time and the invariance of themodels when exposed to a variety of input features and random initialisations,demonstrating differences in the robustness of these approaches.

Automation of Citation Screening for Systematic Literature Reviews using Neural Networks: A Replicability Study