Machine Learning Information Retrieval and Summarisation to Support Systematic Review on Outcomes Based Contracting

TL;DR

This paper explores how machine learning and natural language processing can automate and improve the efficiency of systematic reviews in social sciences, focusing on information retrieval and summarisation to handle large volumes of research.

Contribution

It introduces an integrated ML and NLP approach to automate key stages of systematic reviews, enhancing scalability and efficiency in social science research.

Findings

Automated information retrieval improves review speed.

Summarisation tools assist in managing large research datasets.

Lessons on integrating ML tools into systematic review processes.

Abstract

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.