The rapid growth of biomedical literature presents a major challenge for organizing knowledge and identifying emerging research trends. While PubMed provides effective access to relevant articles, it does not support understanding the conceptual structure of document collections. Existing tools rely on predefined features, ontologies, or parameter-sensitive clustering methods, limiting their ability to uncover fine-grained, data-driven topics in a reproducible manner. We present BioTopicXplor, an on-demand web server for interactive exploration of biomedical literature derived from arbitrary PubMed queries. The system integrates ConvexTopics, a convex optimization?based topic modeling framework that guarantees convergence to a global optimum and eliminates the need for predefined parameters. This enables the generation of reproducible and fine-grained topic structures across large document collections. Given a PubMed query, BioTopicXplor retrieves relevant articles, performs topic discovery, and organizes the resulting subtopics into a hierarchical structure of higher-level themes. To enhance interpretability, the system incorporates large language models to generate concise, literature-grounded summaries and descriptive titles for each topic, with links to supporting evidence. We demonstrate the utility of BioTopicXplor through a case study on anti-aging research, where the system reveals meaningful thematic structures and supports knowledge discovery.

This paper presents guidelines for the annotation of deliberate linguistic metaphor. Expressions that contribute to the same metaphorical image are annotated as a chain along with a semantically contrasting expression of the target domain, which helps to make the domain contrast inherent to metaphor more explicit. So far, a corpus of ten TEDx talks with a total of ca. 20k tokens has been annotated according to these guidelines. 1.35% of the tokens are deliberate metaphorical expressions according to our guidelines, which shows that our guidelines successfully identify a significantly higher proportion of deliberate metaphorical expressions than previous studies.

A search that is targeted at finding a specific document in databases is called a Single Citation search. Single citation searches are particularly important for scholarly databases, such as PubMed, because users are frequently searching for a specific publication. In this work we describe SingleCite, a single citation matching system designed to facilitate user’s search for a specific document. We report on the progress that has been achieved towards building that functionality.