Hospital discharge letters are a fundamental component of patient management, as they provide the crucial information needed for patient post-hospital care. However their creation is very demanding and resource intensive, as it requires consultation of several reports documenting the patient’s journey throughout their hospital stay. Given the increasing pressures on doctor’s time, tools that can draft a reasonable discharge summary, to be then reviewed and finalized by the experts, would be welcome. In this paper we present a comparative study exploring the possibility of automatic generation of discharge summaries within the context of an hospital in an Italian-speaking region and discuss quantitative and qualitative results. Despite some shortcomings, the obtained results show that a generic generative system such as ChatGPT is capable of producing discharge summaries which are relatively close to the human generated ones, even in Italian.

The recognition of mental health’s crucial significance has led to a growing interest in utilizing social media text data in current research trends. However, there remains a significant gap in the study of panic and anxiety on these platforms, despite their high prevalence and severe impact. In this paper, we address this gap by presenting a dataset consisting of 1,930 user posts from Quora and Reddit specifically focusing on panic and anxiety. Through a combination of lexical analysis, emotion detection, and writer attitude assessment, we explore the unique characteristics of each condition. To gain deeper insights, we employ a mental health-specific transformer model and a large language model for qualitative analysis. Our findings not only contribute to the understanding digital discourse on anxiety and panic but also provide valuable resources for the broader research community. We make our dataset, methodologies, and code available to advance understanding and facilitate future studies.

Lexical semantic change detection (also known as semantic shift tracing) is a task of identifying words that have changed their meaning over time. Unsupervised semantic shift tracing, focal point of SemEval2020, is particularly challenging. Given the unsupervised setup, in this work, we propose to identify clusters among different occurrences of each target word, considering these as representatives of different word meanings. As such, disagreements in obtained clusters naturally allow to quantify the level of semantic shift per each target word in four target languages. To leverage this idea, clustering is performed on contextualized (BERT-based) embeddings of word occurrences. The obtained results show that our approach performs well both measured separately (per language) and overall, where we surpass all provided SemEval baselines.