Exploiting Contextual Embeddings to Extract Topic Genealogy from Scientific Literature

Modeling the evolution of topics and forecast future trends is a crucial task when analyzing scientific papers. In this work we propose tASKE (temporal Automated System for Knowledge Extraction), a dynamic topic modeling approach which exploits zero-shot classification and contextual embeddings in order to track topic evolution through time. The approach is evaluated against a corpus of data science papers, assessing the ability of tASKE to correctly classify documents and retrieving relevant derivation relationships between older and new topics in time.