23,856 research outputs found
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
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Strategies used in the pursuit of achievability during goal setting in rehabilitation
We used conversation analysis of six audio- and video-recorded goal setting meetings that were attended by patients and their respective treating team to explore and describe the interaction of participants during interdisciplinary goal setting, and to identify the strategies used to agree goals. The health care professionals involved in the six sessions included four physiotherapists, four occupational therapists, four nurses, one speech and language therapist, and one neuropsychologist. The participants included 3 patients with multiple sclerosis, 2 patients with spinal cord lesions, and 1 patient with stroke from an inpatient neurological rehabilitation unit. Detailed analysis revealed how the treating team shaped the meetings. The most notable finding was that there was rarely a straightforward translation of patient wishes into agreed-on written goals, with the treating team leading goal modification so that goals were achievable. Despite professional dominance, patients also influenced the course of the interaction, particularly when offering resistance to goals proposed by the treating team
Multi-party Interaction in a Virtual Meeting Room
This paper presents an overview of the work carried out at the HMI group of the University of Twente in the domain of multi-party interaction. The process from automatic observations of behavioral aspects through interpretations resulting in recognized behavior is discussed for various modalities and levels. We show how a virtual meeting room can be used for visualization and evaluation of behavioral models as well as a research tool for studying the effect of modified stimuli on the perception of behavior
The Interactional Styles Used by Male and Female Chairpersons in Petra Christian University Student Executive Board Meetings
This study examines the interactional styles related to the role of chairperson used by two female and two male chairpersons in the SEB-PCU meetings. There are three main theories used: interactional styles, gender, and chairpersons and their roles in a meeting. The method used is qualitative approach focusing on the process and the data. The findings reveal that both feminine and masculine interactional styles were used by the chairpersons. The masculine interactional styles were employed to play the roles of chairpersons. The use of interactional styles between female and male chairpersons differs in its ratio although the same linguistic clue was used for the same device. Here, conciliatory feature was not produced by the male chairpersons whereas referentially oriented feature was produced frequently by chairpersons. Overall, it proves that females use more feminine interactional styles while males use more masculine interactional styles. Thus, gender and power play an important role in meeting
From Text to Speech Summarization
In this paper, we present approaches used in text summarization, showing how they can be adapted for speech summarization and where they fall short. Informal style and apparent lack of structure in speech mean that the typical approaches used for text summarization must be extended for use with speech. We illustrate how features derived from speech can help determine summary content within two ongoing summarization projects at Columbia University
Generating Abstractive Summaries from Meeting Transcripts
Summaries of meetings are very important as they convey the essential content
of discussions in a concise form. Generally, it is time consuming to read and
understand the whole documents. Therefore, summaries play an important role as
the readers are interested in only the important context of discussions. In
this work, we address the task of meeting document summarization. Automatic
summarization systems on meeting conversations developed so far have been
primarily extractive, resulting in unacceptable summaries that are hard to
read. The extracted utterances contain disfluencies that affect the quality of
the extractive summaries. To make summaries much more readable, we propose an
approach to generating abstractive summaries by fusing important content from
several utterances. We first separate meeting transcripts into various topic
segments, and then identify the important utterances in each segment using a
supervised learning approach. The important utterances are then combined
together to generate a one-sentence summary. In the text generation step, the
dependency parses of the utterances in each segment are combined together to
create a directed graph. The most informative and well-formed sub-graph
obtained by integer linear programming (ILP) is selected to generate a
one-sentence summary for each topic segment. The ILP formulation reduces
disfluencies by leveraging grammatical relations that are more prominent in
non-conversational style of text, and therefore generates summaries that is
comparable to human-written abstractive summaries. Experimental results show
that our method can generate more informative summaries than the baselines. In
addition, readability assessments by human judges as well as log-likelihood
estimates obtained from the dependency parser show that our generated summaries
are significantly readable and well-formed.Comment: 10 pages, Proceedings of the 2015 ACM Symposium on Document
Engineering, DocEng' 201
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