While evaluating the abstractive summaries offers valuable insights into the model's performance in summarizing the content of materials science literature, it's important to recognize that this assessment alone does not directly confirm the fidelity of the JSON extraction task. However, it's worth noting that abstractive summarization itself is a form of information extraction, albeit on a higher level of abstraction. Therefore, while our evaluation focuses on summarization, it indirectly sheds light on the model's capability to comprehend and extract meaningful information from the input text. This shared aspect of information extraction underscores the interconnectedness of the two tasks and suggests that the proficiency demonstrated in one task can inform our understanding of the model's performance in the other.