Accurate career path prediction can support many stakeholders, like job seekers, recruiters, HR, and project managers. However, publicly available data and tools for career path prediction are scarce. In this work, we introduce Karrierewege, a comprehensive, publicly available dataset containing over 500k career paths, significantly surpassing the size of previously available datasets. We link the dataset to the ESCO taxonomy to offer a valuable resource for predicting career trajectories. To tackle the problem of free-text inputs typically found in resumes, we enhance it by synthesizing job titles and descriptions resulting in Karrierewege+. This allows for accurate predictions from unstructured data, closely aligning with practical application challenges. We benchmark existing state-of-the-art (SOTA) models on our dataset and a previous benchmark and see increased performance and robustness by synthesizing the data for the free-text use cases.

Automatic Term Extraction (ATE) is a critical component in downstream NLP tasks such as document tagging, ontology construction and patent analysis. Current state-of-the-art methods require expensive human annotation and struggle with domain transfer, limiting their practical deployment. This highlights the need for more robust, scalable solutions and realistic evaluation settings. To address this, we introduce a comprehensive benchmark spanning seven diverse domains, enabling performance evaluation at both the document- and corpus-levels. Furthermore, we propose a robust LLM-based model that outperforms both supervised cross-domain encoder models and few-shot learning baselines and performs competitively with its GPT-4o teacher on this benchmark.The first step of our approach is generating psuedo-labels with this black-box LLM on general and scientific domains to ensure generalizability. Building on this data, we fine-tune the first LLMs for ATE. To further enhance document-level consistency, oftentimes needed for downstream tasks, we introduce lightweight post-hoc heuristics. Our approach exceeds previous approaches on 5/7 domains with an average improvement of 10 percentage points. We release our dataset and fine-tuned models to support future research in this area.

Topic models represent topics as ranked term lists, which are often hard to interpret in scientific domains. We explore Topic Description for Scientific Corpora, an approach to generating structured summaries for topic-specific document sets. We propose and investigate two LLM-based pipelines: Selective Context Summarisation (SCS), which uses maximum marginal relevance to select representative documents; and Compressed Context Summarisation (CCS), a hierarchical approach that compresses document sets through iterative summarisation. We evaluate both methods using SUPERT and multi-model LLM-as-a-Judge across three topic modeling backbones and three scientific corpora. Our preliminary results suggest that SCS tends to outperform CCS in quality and robustness, while CCS shows potential advantages on larger topics. Our findings highlight interesting trade-offs between selective and compressed strategies for topic-level summarisation in scientific domains. We release code and data for two of the three datasets.