Our findings highlight the efficiency and effectiveness of our semi-automated annotation process in maintaining high levels of accuracy and quality. By maintaining a high level of accuracy and quality in the extracted information, our approach minimizes the need for extensive manual annotation from scratch, thus streamlining the process compared to traditional full manual approaches. The model's proficiency in handling materials science-specific nuances further enhances its potential for automating the annotation of materials science literature. These results highlight the effectiveness of our approach in accelerating the generation of structured information and abstractive summaries from scientific texts, paving the way for more efficient and scalable annotation workflows in materials science research.