Scientific literature is a crucial resource for driving scientific research and technological advancement. However, with the explosive growth of literature, researchers face challenges in quickly obtaining key information from massive documents. This paper proposes an open information extraction model MMOIE (Multi-Answer Machine-Reading Comprehension Open Information Extraction) based on machine reading comprehension to efficiently extract triplets from scientific literature. By combining the SIFRank $^+$ model with the ELMo pre-trained language model, the model accurately calculates the weight of keyword importance and filters out factual triplets containing at least one keyword. Experimental results show that compared to methods like ZORE, SpanOIE, MGD-GNN, and TPOIE, MMOIE achieves a recall rate of $64.78\%$ and an F1 score of $55.62\%$ in triplet extraction, significantly improving extraction efficiency and quality. It effectively captures entity relationships in literature, providing strong support for researchers to quickly access key information.
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