Machine Learning Information Retrieval and Summarisation to Support Systematic Review on Outcomes Based Contracting
This addresses the problem of labor-intensive systematic reviews for researchers in social sciences, though it appears incremental in applying existing ML/NLP methods to this domain.
The study tackled the challenge of handling large volumes of academic literature in systematic reviews by using machine learning and natural language processing tools to automate time-intensive stages, resulting in enhanced efficiency and scope for social science reviews.
As academic literature proliferates, traditional review methods are increasingly challenged by the sheer volume and diversity of available research. This article presents a study that aims to address these challenges by enhancing the efficiency and scope of systematic reviews in the social sciences through advanced machine learning (ML) and natural language processing (NLP) tools. In particular, we focus on automating stages within the systematic reviewing process that are time-intensive and repetitive for human annotators and which lend themselves to immediate scalability through tools such as information retrieval and summarisation guided by expert advice. The article concludes with a summary of lessons learnt regarding the integrated approach towards systematic reviews and future directions for improvement, including explainability.