Ronald Böck

Nowadays, individuals can be overwhelmed by a huge number of documents being present in daily life. Capturing the necessary details is often a challenge. Therefore, it is rather important to summarize documents to obtain the main information quickly. There currently exist automatic approaches to this task, but their quality is often not properly assessed. State-of-the-art metrics rely on human-generated summaries as a reference for the evaluation. If no reference is given, the assessment will be challenging. Therefore, in the absence of human-generated reference summaries, we investigated an alternative approach to how machine-generated summaries can be evaluated. For this, we focus on the original text or document to retrieve a metric that allows a direct evaluation of automatically generated summaries. This approach is particularly helpful in cases where it is difficult or costly to find reference summaries. In this paper, we present a novel metric called Summary Score without Reference—SUSWIR—which is based on four factors already known in the text summarization community: Semantic Similarity, Redundancy, Relevance, and Bias Avoidance Analysis, overcoming drawbacks of common metrics. Therefore, we aim to close a gap in the current evaluation environment for machine-generated text summaries. The novel metric is introduced theoretically and tested on five datasets from their respective domains. The conducted experiments yielded noteworthy outcomes, employing the utilization of SUSWIR.

Humans are considered to be communicative, usually interacting in dyads or groups. In this paper, we investigate group interactions regarding performance in a rather formal gathering. In particular, a collection of ten performance indicators used in social group sciences is used to assess the outcomes of the meetings in this manuscript, in an automatic, machine learning-based way. For this, the Parking Lot Corpus, comprising 70 meetings in total, is analysed. At first, we obtain baseline results for the automatic prediction of performance results on the corpus. This is the first time the Parking Lot Corpus is tapped in this sense. Additionally, we compare baseline values to those obtained, utilising bidirectional long-short term memories. For multiple performance indicators, improvements in the baseline results are able to be achieved. Furthermore, the experiments showed a trend that the acoustic material of the remaining group should use for the prediction of team performance.

Editorial: Group analysis and multi-agent interaction—Bridging the gap between social investigations and computational analysis

Typical current assistance systems often take the form of optimised user interfaces between the user interest and the capabilities of the system. In contrast, a peer-like system should be capable of independent decision-making capabilities, which in turn require an understanding and knowledge of the current situation for performing a sensible decision-making process. We present a method for a system capable of interacting with their user to optimise their information-gathering task, while at the same time ensuring the necessary satisfaction with the system, so that the user may not be discouraged from further interaction. Based on this collected information, the system may then create and employ a specifically adapted rule-set base which is much closer to an intelligent companion than a typical technical user interface. A further aspect is the perception of the system as a trustworthy and understandable partner, allowing an empathetic understanding between the user and the system, leading to a closer integrated smart environment.

This review presents, in a tutorial-like manner, the current state of research in group affects. We focus on the automatic processing of affects and their estimation, which is, of course, based on foundations in social sciences regarding multiple modalities. In addition, we highlight recent developments in methods and classification approaches. Further, a collection of suitable corpora for affect estimation is presented. Based on the literature, we discuss current achievements in relation to parameters that influence the processing of groups. Finally, in the sense of a road map, different perspectives on further research are presented.