Generating automated meeting summaries

The thesis at hand introduces a novel approach for the generation of abstractive summaries of meetings. While the automatic generation of document summaries has been studied for some decades now, the novelty of this thesis is mainly the application to the meeting domain (instead of text documents) as well as the use of a lexicalized representation formalism on the basis of Frame Semantics. This allows us to generate summaries abstractively (instead of extractively).Die vorliegende Arbeit stellt einen neuartigen Ansatz zur Generierung abstraktiver Zusammenfassungen von Gruppenbesprechungen vor. Während automatische Textzusammenfassungen bereits seit einigen Jahrzehnten erforscht werden, liegt die Neuheit dieser Arbeit vor allem in der Anwendungsdomäne (Gruppenbesprechungen statt Textdokumenten), sowie der Verwendung eines lexikalisierten Repräsentationsformulism auf der Basis von Frame-Semantiken, der es erlaubt, Zusammenfassungen abstraktiv (statt extraktiv) zu generieren. Wir argumentieren, dass abstraktive Ansätze für die Zusammenfassung spontansprachlicher Interaktionen besser geeignet sind als extraktive

Toward summarization of communicative activities in spoken conversation

This thesis is an inquiry into the nature and structure of face-to-face conversation, with a special focus on group meetings in the workplace. I argue that conversations are composed of episodes, each of which corresponds to an identifiable communicative activity such as giving instructions or telling a story. These activities are important because they are part of participants’ commonsense understanding of what happens in a conversation. They appear in natural summaries of conversations such as meeting minutes, and participants talk about them within the conversation itself. Episodic communicative activities therefore represent an essential component of practical, commonsense descriptions of conversations. The thesis objective is to provide a deeper understanding of how such activities may be recognized and differentiated from one another, and to develop a computational method for doing so automatically. The experiments are thus intended as initial steps toward future applications that will require analysis of such activities, such as an automatic minute-taker for workplace meetings, a browser for broadcast news archives, or an automatic decision mapper for planning interactions. My main theoretical contribution is to propose a novel analytical framework called participant relational analysis. The proposal argues that communicative activities are principally indicated through participant-relational features, i.e., expressions of relationships between participants and the dialogue. Participant-relational features, such as subjective language, verbal reference to the participants, and the distribution of speech activity amongst the participants, are therefore argued to be a principal means for analyzing the nature and structure of communicative activities. I then apply the proposed framework to two computational problems: automatic discourse segmentation and automatic discourse segment labeling. The first set of experiments test whether participant-relational features can serve as a basis for automatically segmenting conversations into discourse segments, e.g., activity episodes. Results show that they are effective across different levels of segmentation and different corpora, and indeed sometimes more effective than the commonly-used method of using semantic links between content words, i.e., lexical cohesion. They also show that feature performance is highly dependent on segment type, suggesting that human-annotated “topic segments” are in fact a multi-dimensional, heterogeneous collection of topic and activity-oriented units. Analysis of commonly used evaluation measures, performed in conjunction with the segmentation experiments, reveals that they fail to penalize substantially defective results due to inherent biases in the measures. I therefore preface the experiments with a comprehensive analysis of these biases and a proposal for a novel evaluation measure. A reevaluation of state-of-the-art segmentation algorithms using the novel measure produces substantially different results from previous studies. This raises serious questions about the effectiveness of some state-of-the-art algorithms and helps to identify the most appropriate ones to employ in the subsequent experiments. I also preface the experiments with an investigation of participant reference, an important type of participant-relational feature. I propose an annotation scheme with novel distinctions for vagueness, discourse function, and addressing-based referent inclusion, each of which are assessed for inter-coder reliability. The produced dataset includes annotations of 11,000 occasions of person-referring. The second set of experiments concern the use of participant-relational features to automatically identify labels for discourse segments. In contrast to assigning semantic topic labels, such as topical headlines, the proposed algorithm automatically labels segments according to activity type, e.g., presentation, discussion, and evaluation. The method is unsupervised and does not learn from annotated ground truth labels. Rather, it induces the labels through correlations between discourse segment boundaries and the occurrence of bracketing meta-discourse, i.e., occasions when the participants talk explicitly about what has just occurred or what is about to occur. Results show that bracketing meta-discourse is an effective basis for identifying some labels automatically, but that its use is limited if global correlations to segment features are not employed. This thesis addresses important pre-requisites to the automatic summarization of conversation. What I provide is a novel activity-oriented perspective on how summarization should be approached, and a novel participant-relational approach to conversational analysis. The experimental results show that analysis of participant-relational features is

Proceedings of the Eighth Italian Conference on Computational Linguistics CliC-it 2021

The eighth edition of the Italian Conference on Computational Linguistics (CLiC-it 2021) was held at Università degli Studi di Milano-Bicocca from 26th to 28th January 2022. After the edition of 2020, which was held in fully virtual mode due to the health emergency related to Covid-19, CLiC-it 2021 represented the first moment for the Italian research community of Computational Linguistics to meet in person after more than one year of full/partial lockdown