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The fast-growing number of research articles makes it problematic forscholars to keep track of the new findings related to their areas of expertise.Furthermore, linking knowledge across disciplines in rapidly developing fieldsbecomes challenging for complex topics like climate change that demandinterdisciplinary solutions. At the same time, the rise of Black Box types oftext summarization makes it difficult to understand how text relationships arebuilt, let alone relate to existing theories conceptualizing cause-effectrelationships and permitting hypothesizing. This work aims to sensibly useNatural Language Processing by extracting variables relations and synthesizingtheir findings using networks while relating to key concepts dominant inrelevant disciplines. As an example, we apply our methodology to the analysisof farmers' adaptation to climate change. For this, we perform a NaturalLanguage Processing analysis of publications returned by Scopus in August 2022.Results show that the use of Natural Language Processing together with networksin a descriptive manner offers a fast and interpretable way to synthesizeliterature review findings as long as researchers back up results with theory.

Using Natural Language Processing and Networks to Automate Structured Literature Reviews: An Application to Farmers Climate Change Adaptation