## 5 Discussion
### Limitations and Opportunities  
In this paper, we are able to demonstrate the effectiveness of the MST approach in industrial settings. In the MST approach, the trigger is detected first and the cause and effect are then identified on a given trigger. However, the method uses an OCR for extracting texts from unstructured documents with visual elements such as presentation slides. Therefore, it is limited to the extraction of textualized causal relations in presentation slides and cannot process causal information expressed through visual elements like arrows, color encoding, etc. This limitation is of high relevance as presentation slides are created to be viewed and not be simply read, since a great amount of their information is in visualized form. This information is lost when converting the content into plain text. Furthermore, the content extraction via OCR is prone to errors. In particular, tabular structures, line breaks, hyphenated words, small fonts, and images with blurry texts can pose problems for the OCR. To address these limitations, it is necessary to explore hybrid approaches in which NLP approaches are augmented with computer vision methods.  
The proposed method aims to extract causal information from historic archived industrial documents without imposing significant limitations on scalability. However, considering the diversity of data sets, it may be necessary to adapt the language model used. For instance, supporting transfer learning between different languages, as highlighted in [41], could be beneficial. Additionally, annotating a representative sample of the data set may be required, as this method follows a supervised learning approach. These scenarios were not addressed in the provided case study.  
Additionally, the multi-stage approach only allows us to extract causal relations which are expressed through explicit linguistic triggers. Causes and effects without an explicit trigger cannot be detected, since the trigger is given as an input to detect the cause and effect in the second stage of the NER. Even though the chances for two elements to be perceived as causally related by the annotators are lower the further away they are from each other in the text [42], it is possible to have causal relations spanning over a few sentences or even a whole document. To extract such relations, inter-sentential or intradocument relation extraction is necessary. Our method focuses on intra-text causality. One text corresponds to the content of one text box or bullet point in terms of presentation slides or one cell in terms of content from tabular formats. This means that this method is not able to detect causal relations between elements from different text boxes or cells. Another drawback of this approach is its dependence on human annotations. This reliance raises not just considerations of resource allocation--given the time and cost associated with large-scale human annotation--but also presents the daunting task of resolving varying interpretations of causality among different annotators. While the suggested guidelines hold the potential to serve as prompts for sophisticated language models in the future, their practical implementation is yet to be fully assessed. It is essential to undertake more in-depth research to explore the viability of this approach, particularly with a keen focus on addressing potential information security and reliability concerns.  
Finally, this research primarily focuses on extracting causal relationships described in text. Terms such as \"distal\" and \"proximal\", as well as \"sufficient\" and \"necessary\", are integral to modeling causal domain knowledge. Research on causal domain knowledge represents an important aspect for future work, especially in industrial settings.