We propose SemCSE-Multi, a novel unsupervised framework for generating multifaceted embeddings of scientific abstracts, evaluated in the domains of invasion biology and medicine. These embeddings capture distinct, individually specifiable aspects in isolation, thus enabling fine-grained and controllable similarity assessments as well as adaptive, user-driven visualizations of scientific domains. Our approach relies on an unsupervised procedure that produces aspect-specific summarizing sentences and trains embedding models to map semantically related summaries to nearby positions in the embedding space. We then distill these aspect-specific embedding capabilities into a unified embedding model that directly predicts multiple aspect embeddings from a scientific abstract in a single, efficient forward pass. In addition, we introduce an embedding decoding pipeline that decodes embeddings back into natural language descriptions of their associated aspects. Notably, we show that this decoding remains effective even for unoccupied regions in low-dimensional visualizations, thus offering vastly improved interpretability in user-centric settings.

翻译：我们提出了SemCSE-Multi，一个用于生成科学摘要多面性嵌入的新型无监督框架，并在入侵生物学和医学领域进行了评估。这些嵌入能够分别捕获独立且可单独指定的不同方面，从而实现细粒度且可控的相似性评估，以及科学领域的自适应、用户驱动的可视化。我们的方法依赖于一种无监督流程，该流程生成特定方面的概括性句子，并训练嵌入模型将语义相关的摘要映射到嵌入空间中相邻的位置。随后，我们将这些特定方面的嵌入能力提炼到一个统一的嵌入模型中，该模型能够通过一次高效的前向传播，直接从科学摘要中预测出多个方面的嵌入。此外，我们引入了一个嵌入解码流程，可将嵌入解码回其相关方面的自然语言描述。值得注意的是，我们证明了即使在低维可视化的未占据区域，这种解码仍然有效，从而在以用户为中心的场景中提供了显著提升的可解释性。