SemCSE-Multi: Multifaceted and Decodable Embeddings for Aspect-Specific and Interpretable Scientific Domain Mapping

2025-10-13

AI Summary

To address the lack of multifaceted representation and interpretability in scientific abstract embeddings, this paper proposes SemCSE-Multi, an unsupervised framework. It constructs multi-task training objectives by generating aspect-specific summary sentences and jointly learns fine-grained, disentangled, multidimensional embeddings within a unified encoder. Innovatively integrating knowledge distillation with natural language decoding, it produces low-dimensional, decodable, and interpretable embeddings in a single forward pass while enhancing semantic readability in underrepresented regions. Evaluated on invasive biology and medical domains, SemCSE-Multi enables user-driven, controllable similarity analysis and domain-specific visualization. Results demonstrate significant improvements in domain adaptability, interpretability, and downstream utility of the embeddings.

Technology Category

• Machine Learning: Multimodal Learning
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.
• Computer Vision: Multi-modal Vision

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

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.

Problem

• Generating multifaceted embeddings for scientific abstracts
• Enabling fine-grained controllable similarity assessments
• Decoding embeddings into interpretable natural language descriptions

Innovation

• Generates multifaceted embeddings for scientific abstracts
• Unified model predicts multiple aspect embeddings efficiently
• Decodes embeddings into natural language for interpretability