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PubMed Sentence Matcher is a standalone, high-accuracy system designed to pinpoint the most relevant sentences within PubMed articles based on an input query. It employs a sophisticated 6-stage processing pipeline, leveraging LLMs for query optimization and result verification. The system combines multi-signal similarity metrics—including semantic embeddings, keyword overlap, structural analysis, and contextual relevance—to ensure precise matches. Researchers can generate citations in multiple formats (MLA, APA, Nature) directly from the matched sentences, streamlining the literature review and citation process.
