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

• URL: http://arxiv.org/abs/2412.08578v1
• Date: Wed, 11 Dec 2024 17:54:01 GMT
• Title: Machine Learning Information Retrieval and Summarisation to Support Systematic Review on Outcomes Based Contracting
• Authors: Iman Munire Bilal, Zheng Fang, Miguel Arana-Catania, Felix-Anselm van Lier, Juliana Outes Velarde, Harry Bregazzi, Eleanor Carter, Mara Airoldi, Rob Procter,
• Abstract summary: 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.
• Score: 7.081184240581488
• License:
• 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.

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