Automated Literature Based Discovery (LBD) generates new knowledge by combining what is already known in literature. Facilitating large-scale hypothesis testing and generation from huge collections of literature, LBD could significantly support research in biomedical sciences. However, the uptake of LBD by the scientific community has been limited. One of the key reasons for this is the limited nature of existing LBD methodology. Based on fairly shallow methods, current LBD captures only some of the information available in literature. We discuss how advanced Text Mining based on Information retrieval, Natural Language Processing and data mining could open the doors to much deeper, wider coverage and dynamic LBD better capable of evolving with science, in particular when combined with sophisticated, state-of-the-art knowledge discovery techniques.