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