Abstract
In academic writing, bibliography compilations is essential but time-consuming, often requiring repeated searches for references. Hence, an efficient tool for faster bibliography compilation is needed. Our work offers a solution to the challenges of managing large-scale bibliographic databases, introducing a new algorithm that improves both efficiency and sensitivity. Using two-vector semantic modelling, bibliographic entries and queries are embedded into the same vector space to select relevant references based on semantic similarity. Experimental results with 3.37 million entries show the method reduces the time needed to generate a manageable subset, streamlining scholarly writing. Our code and dataset are publicly available at https://github.com/cestwc/bibliography-pre-selection.