LAUSR

The fast development of science and technology is accompanied by the booming of cutting edge research. Researchers need to digest more and more recently published publications in order to keep themselves up to date. This becomes tough in particular with the prevalence of preprint publishing such as arXiv, where inspiring works could come out without being peer-reviewed. Is that possible to design an automatic system to help researchers quickly gain a glimpse of a piece of work or gain useful background knowledge for deeply understanding it? To this end, we proposed a practical framework called Master Reading Tree (MRT) to trace the evolution of scientific publications. In this framework, we can build annotated evolution roadmaps for publications and identify important previous works or evolution tracks by generating expressive embeddings and clustering them into various groups. With comprehensive evaluations, our proposed framework demonstrates its superior capability in capturing underlying relations behind publications over several baseline algorithms. Finally, we integrated the proposed MRT framework on AMiner, an online academic platform, where users can generate roadmaps using MRT for free and their interactions are further used to refine the model.