Entropy-based Sampling for Abstractive Multi-document Summarization in Low-resource Settings

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 infor- mation entropy that allow us to perform MDS in a single stage. These approaches also con- sider all source documents without using MDS training data nor changing the model’s archi- tecture. 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 re- maining primarily abstractive. We release our code and MDS test set to encourage further research in German abstractive MDS

German also Hallucinates! Inconsistency Detection in News Summaries with the Absinth Dataset

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 n hallucination detection in German

Towards Recommender Systems in Augmented Reality for Tourism

Recent advances in augmented reality have enabled new ways of generating and presenting item recommendations. In tourism, AR applications can, for example, enhance points of interests (POIs) with virtual elements in AR and provide tourists with personalized recommendations for places to visit. In this paper, we present our prototype: a touristic AR application that augments various POIs with digital content and generates context-aware recommendations for POIs in the Niederdorf old town of Zurich, Switzerland. We demonstrate how useful information can be presented to users in an engaging way by combining AR technologies and recommender systems.ISSN:2198-724

Talking Houses: Transforming Touristic Buildings into Intelligent Characters in Augmented Reality

Augmented reality (AR) technologies can enhance the user's experience of visiting attractions, shops, and restaurants by using AR-based virtual elements and additional information about the places they are visiting. In this work, we transform the city landscape or iconic buildings into a unique experience by bringing iconic characters onto the buildings to increase users' engagement. Our techniques transform buildings or parts of a building into a virtual character with which the user can interact. We designed two unique experiences: (a) 'The Square' in which the character will talk about the building's history and other anecdotes about the area, and (b) 'The Hunt' in which the user is involved in a scavenger hunt where they have to identify buildings using the hints given by virtual characters. We have conducted a live user study to assess our prototype's usability. Our preliminary experimental results demonstrated that our prototype has high usability and users using our system felt a pleasant and enjoyable experience.ISSN:2198-724