The advent of Large Language Models (LLMs) has led to remarkable progress on a wide range of natural language processing tasks. Despite the advances, these large-sized models still suffer from hallucinating information in their output, which poses a major issue in automatic text summarization, as we must guarantee that the generated summary is consistent with the content of the source document. Previous research addresses the challenging task of detecting hallucinations in the output (i.e. inconsistency detection) in order to evaluate the faithfulness of the generated summaries. However, these works primarily focus on English and recent multilingual approaches lack German data. This work presents Absinth, a manually annotated dataset for hallucination detection in German news summarization and explores the capabilities of novel open-source LLMs on this task in both fine-tuning and in-context learning settings. We open-source and release the Absinth dataset to foster further research on hallucination detection in German.

Research in Multi-document Summarization (MDS) mostly focuses on the English language and depends on large MDS datasets that are not available for other languages.Some of these approaches concatenate the source documents, resulting in overlong model inputs.Existing transformer architectures are unable to process such long inputs entirely, omitting documents in the summarization process.Other solutions address this issue by implementing multi-stage approaches that also require changes in the model architecture.In this paper, we introduce various sampling approaches based on information entropy that allow us to perform MDS in a single stage.These approaches also consider all source documents without using MDS training data nor changing the model's architecture.Besides, we build a MDS test set of German news articles to assess the performance of our methods on abstractive multi-document summaries.Experimental results show that our entropy-based approaches outperform previous state-of-the-art on German MDS, while still remaining primarily abstractive.We release our code 1 and MDS test set 2 to encourage further research in German abstractive MDS.

The rapid advances and increasing accessibility of augmented reality (AR) in recent years opened up many new possibilities to incorporate AR into our daily lives. A very interesting area for AR is tourism where one can enhance attractions with virtual elements and provide tourists with additional information about the places they are visiting. In this paper, we present our prototype, an AR application that augments various points of interest (POIs) by showing images and facts about each POI. We also developed a simple recommender system that ensures the facts are selected based on user preferences, thus creating a unique and personalized experience for each user. Furthermore, we also conducted a live user study to assess the usability of our prototype and the usefulness of our personalization system.