In evaluating structured JSON extraction, we encountered significant complexity stemming from the diverse types of entries found in materials science literature. Each JSON entry encapsulates a unique facet of scientific information, ranging from chemical compositions and processing conditions to characterization methods and performance properties. This diversity poses a significant challenge in devising a singular metric to comprehensively evaluate the fidelity of the extracted data. The heterogeneous nature of structured data complicates the application of traditional evaluation metrics such as F1 score, ROUGE, BERTScore. Thus, quantifying the accuracy of JSON extraction proves to be a multifaceted task, demanding a more nuanced approach. While our initial attempt to establish a definitive evaluation metric encountered obstacles, we recognize the importance of devising an appropriate assessment framework. In future endeavors, we aim to develop a robust metric tailored to the intricacies of structured JSON extraction tasks like this, facilitating a more comprehensive evaluation of the model's performance in handling structured data. In the interim, we focused our evaluation efforts on the abstractive summarization task, which offered clearer insights into the model's performance and the requisite level of human intervention for data annotation.