A roadmap toward the automatic composition of systematic literature reviews

Abstract

Objective. This paper presents an overview of existing artificial intelligence tools to produce systematic literature reviews. Furthermore, we propose a general framework resulting from combining these techniques to highlight the challenges and possibilities currently existing in this research area. Design/Methodology/Approach. We undertook a scoping review on the systematic literature review steps to automate them via computational techniques. Results/Discussion. The process of creating a literature review is both creative and technical. The technical part of this process is liable to automation. Based on the literature, we chose to divide this technical part into four steps: searching, screening, extraction, and synthesis. For each one of these steps, we presented practical artificial intelligence techniques to carry them out. In addition, we presented the obstacles encountered in the application of each technique. Conclusion. We proposed a framework for automatically creating systematic literature reviews by combining and placing existing techniques in stages where they possess the greatest potential to be useful. Despite still lacking practical assessment in different areas of knowledge, this proposal indicates ways with the potential to reduce the time-consuming and repetitive work embedded in the systematic literature review process. Originality/Value. The paper presents the current possibilities for automating systematic literature reviews and how they can work together to reduce researchers’ operational workload.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 41 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.