Pro-active Meeting Assistants : Attention Please!

This paper gives an overview of pro-active meeting assistants, what they are and when they can be useful. We explain how to develop such assistants with respect to requirement definitions and elaborate on a set of Wizard of Oz experiments, aiming to find out in which form a meeting assistant should operate to be accepted by participants and whether the meeting effectiveness and efficiency can be improved by an assistant at all

Pro-active Meeting Assistants: Attention Please!

This paper gives an overview of pro-active meeting assistants, what they are and when they can be useful. We explain how to develop such assistants with respect to requirement definitions and elaborate on a set of Wizard of Oz experiments, aiming to find out in which form a meeting assistant should operate to be accepted by participants and whether the meeting effectiveness and efficiency can be improved by an assistant at all. This paper gives an overview of pro-active meeting assistants, what they are and when they can be useful. We explain how to develop such assistants with respect to requirement definitions and elaborate on a set of Wizard of Oz experiments, aiming to find out in which form a meeting assistant should operate to be accepted by participants and whether the meeting effectiveness and efficiency can be improved by an assistant at all

Query types in the meeting domain: assessing the role of argumentative structure in answering questions on meeting discussion records

We define a new task of question answering on meeting records and assess its difficulty in terms of types of information and retrieval techniques required. The importance of this task is revealed by the increasingly growing interest in the design of sophisticated interfaces for accessing meeting records such as meeting browsers. We ground our work on the empirical analysis of elicited user queries. We assess what is the type of information sought by the users and perform a user query classification along several semantic dimensions of the meeting content. We found that queries about argumentative processes and outcomes represent the majority among the elicited queries (about 60%). We also assess the difficulty in answering the queries and focus on the requirements of a prospective QA system to successfully deal with them. Our results suggest that standard Information Retrieval and Question Answering alone can only account for less than 20% of the queries and need to be completed with additional type of information and inference

The Necessity of a Meeting Recording and Playback System, and the Benefit of Topic–Level Annotations to Meeting Browsing

Much work in the area of Computer Supported Cooperative Work (CSCW) has targeted the problem of supporting meetings between collaborators who are non-collocated, enabling meetings to transcend boundaries of space. In this paper, we explore the beginnings of a proposed solution for allowing meetings to transcend time as well. The need for such a solution is motivated by a user survey in which busy professionals are questioned about meetings they have either missed or forgotten the important details about after the fact. Our proposed solution allows these professionals to transcend time in a sense by revisiting a recorded meeting that has been structured for quick retrieval of sought information. Such a solution supports complete recovery of prior discussions, allowing needed information to be retrieved quickly, and thus potentially facilitating the effective continuation of discussions from the past. We evaluate the proposed solution with a formal user study in which we measure the impact of the proposed structural annotations on retrieval of information. The results of the study show that participants took significantly less time to retrieve the answers when they had access to discourse structure based annotation than in a control condition in which they had access only to unannotated video recordings (p < 0.01, effect size 0.94 standard deviations).</p

Automatic social role recognition and its application in structuring multiparty interactions

Automatic processing of multiparty interactions is a research domain with important applications in content browsing, summarization and information retrieval. In recent years, several works have been devoted to find regular patterns which speakers exhibit in a multiparty interaction also known as social roles. Most of the research in literature has generally focused on recognition of scenario specific formal roles. More recently, role coding schemes based on informal social roles have been proposed in literature, defining roles based on the behavior speakers have in the functioning of a small group interaction. Informal social roles represent a flexible classification scheme that can generalize across different scenarios of multiparty interaction. In this thesis, we focus on automatic recognition of informal social roles and exploit the influence of informal social roles on speaker behavior for structuring multiparty interactions. To model speaker behavior, we systematically explore various verbal and non verbal cues extracted from turn taking patterns, vocal expression and linguistic style. The influence of social roles on the behavior cues exhibited by a speaker is modeled using a discriminative approach based on conditional random fields. Experiments performed on several hours of meeting data reveal that classification using conditional random fields improves the role recognition performance. We demonstrate the effectiveness of our approach by evaluating it on previously unseen scenarios of multiparty interaction. Furthermore, we also consider whether formal roles and informal roles can be automatically predicted by the same verbal and nonverbal features. We exploit the influence of social roles on turn taking patterns to improve speaker diarization under distant microphone condition. Our work extends the Hidden Markov model (HMM)- Gaussian mixture model (GMM) speaker diarization system, and is based on jointly estimating both the speaker segmentation and social roles in an audio recording. We modify the minimum duration constraint in HMM-GMM diarization system by using role information to model the expected duration of speaker's turn. We also use social role n-grams as prior information to model speaker interaction patterns. Finally, we demonstrate the application of social roles for the problem of topic segmentation in meetings. We exploit our findings that social roles can dynamically change in conversations and use this information to predict topic changes in meetings. We also present an unsupervised method for topic segmentation which combines social roles and lexical cohesion. Experimental results show that social roles improve performance of both speaker diarization and topic segmentation