Meta Comments for Summarizing Meeting Speech

Abstract. This paper is about the extractive summarization of meeting speech, using the ICSI and AMI corpora. In the first set of experiments we use prosodic, lexical, structural and speaker-related features to select the most informative dialogue acts from each meeting, with the hypothesis being that such a rich mixture of features will yield the best results. In the second part, we present an approach in which the identification of “meta-comments ” is used to create more informative summaries that provide an increased level of abstraction. We find that the inclusion of these meta comments improves summarization performance according to several evaluation metrics.

Summarizing Dialogic Arguments from Social Media

Online argumentative dialog is a rich source of information on popular beliefs and opinions that could be useful to companies as well as governmental or public policy agencies. Compact, easy to read, summaries of these dialogues would thus be highly valuable. A priori, it is not even clear what form such a summary should take. Previous work on summarization has primarily focused on summarizing written texts, where the notion of an abstract of the text is well defined. We collect gold standard training data consisting of five human summaries for each of 161 dialogues on the topics of Gay Marriage, Gun Control and Abortion. We present several different computational models aimed at identifying segments of the dialogues whose content should be used for the summary, using linguistic features and Word2vec features with both SVMs and Bidirectional LSTMs. We show that we can identify the most important arguments by using the dialog context with a best F-measure of 0.74 for gun control, 0.71 for gay marriage, and 0.67 for abortion.Comment: Proceedings of the 21th Workshop on the Semantics and Pragmatics of Dialogue (SemDial 2017

Sentiment and behaviour annotation in a corpus of dialogue summaries

This paper proposes a scheme for sentiment annotation. We show how the task can be made tractable by focusing on one of the many aspects of sentiment: sentiment as it is recorded in behaviour reports of people and their interactions. Together with a number of measures for supporting the reliable application of the scheme, this allows us to obtain sufficient to good agreement scores (in terms of Krippendorf's alpha) on three key dimensions: polarity, evaluated party and type of clause. Evaluation of the scheme is carried out through the annotation of an existing corpus of dialogue summaries (in English and Portuguese) by nine annotators. Our contribution to the field is twofold: (i) a reliable multi-dimensional annotation scheme for sentiment in behaviour reports; and (ii) an annotated corpus that was used for testing the reliability of the scheme and which is made available to the research community

Foreground and background text in retrieval

Our hypothesis is that certain clauses have foreground functions in text, while other clauses have background functions and that these functions are expressed or reflected in the syntactic structure of the clause. Presumably these clauses will have differing utility for automatic approaches to text understanding; a summarization system might want to utilize background clauses to capture commonalities between numbers of documents while an indexing system might use foreground clauses in order to capture specific characteristics of a certain document

Does gender matter in doctor-patient communication during standard gynaecological consultations? : an analysis using mixed methods

This paper assesses whether gender plays a role when male and female participants discuss the quality of doctor\u2013patient communication in gynaecological consultations. A European multi-centre study was conducted comprising 259 participants in 35 gender- and country-specific focus groups. In all focus groups, a set of four videotaped Objective Structured Clinical Examination (OSCE) consultations was used as a prompt for discussion. The doctors\u2019 ability in communication was assessed by participants\u2019 ratings and by a quantified content analysis of their comments, using a mixed-method approach. Gender analysis was performed applying a set of generalized linear regression models. The findings indicated that gender differences were smaller than expected. The individual ratings of the overall quality of communication were similar for male and female participants, and there were hardly any differences in the content of the discussions. The only two exceptions were that female doctors were criticized more than male doctors when they made impersonal comments and that female participants were more outspoken than men, positively and negatively. The prevalence of gender similarities suggests that doctors\u2019 empathy, support, understanding and pleasantness are highly appreciated by both male and female participants and appear to transcend gender differences

Text Mining Infrastructure in R

During the last decade text mining has become a widely used discipline utilizing statistical and machine learning methods. We present the tm package which provides a framework for text mining applications within R. We give a survey on text mining facilities in R and explain how typical application tasks can be carried out using our framework. We present techniques for count-based analysis methods, text clustering, text classification and string kernels.

The Minnesota News Council: Principles, Precedent and Moral Authority

This study addresses the Minnesota News Council’s moral authority–that is, its ability to serve as a referent for the ethical or moral choices of others–and how its authority might be affected by perceptions of its legitimacy. After analyzing all of the Council’s 125 written determinations, we argue that the Council’s legitimacy and authority could be enlarged by clearer statements of ethical principles, explicit expressions of standards of conduct, and more consistent references to past determinations