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.

翻译：快速增长的科研论文数量使得学者难以系统追踪其专业领域的最新发现。此外，在气候变化这类亟需跨学科解决方案的复杂议题中，建立跨领域知识关联变得尤为困难。与此同时，"黑箱式"文本摘要技术的兴起，既遮蔽了文本关系构建的内在机理，更难以关联到阐述因果关系、支持假设检验的既有理论。本研究旨在通过提取变量关系并利用网络方法整合研究成果，在关联相关学科核心概念的同时，实现自然语言处理的合理化应用。我们以农民气候变化适应行为分析为案例，对2022年8月Scopus数据库收录的文献进行自然语言处理分析。结果表明，只要研究者能够用理论支撑研究发现，采用自然语言处理与网络结合的描述性方法，就能快速且可解释地整合文献综述成果。