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.

Machine-learned models for author profiling in social media often rely on data acquired via self-reporting-based psychometric tests (questionnaires) filled out by social media users. This is an expensive but accurate data collection strategy. Another, less costly alternative, which leads to potentially more noisy and biased data, is to rely on labels inferred from publicly available information in the profiles of the users, for instance self-reported diagnoses or test results. In this paper, we explore a third strategy, namely to directly use a corpus of items from validated psychometric tests as training data. Items from psychometric tests often consist of sentences from an I-perspective (e.g., ‘I make friends easily.’). Such corpora of test items constitute ‘small data’, but their availability for many concepts is a rich resource. We investigate this approach for personality profiling, and evaluate BERT classifiers fine-tuned on such psychometric test items for the big five personality traits (openness, conscientiousness, extraversion, agreeableness, neuroticism) and analyze various augmentation strategies regarding their potential to address the challenges coming with such a small corpus. Our evaluation on a publicly available Twitter corpus shows a comparable performance to in-domain training for 4/5 personality traits with T5-based data augmentation.