40 research outputs found
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. WĂ€hrend automatische Textzusammenfassungen bereits seit einigen Jahrzehnten erforscht werden, liegt die Neuheit dieser Arbeit vor allem in der AnwendungsdomĂ€ne (Gruppenbesprechungen statt Textdokumenten), sowie der Verwendung eines lexikalisierten ReprĂ€sentationsformulism auf der Basis von Frame-Semantiken, der es erlaubt, Zusammenfassungen abstraktiv (statt extraktiv) zu generieren. Wir argumentieren, dass abstraktive AnsĂ€tze fĂŒr die Zusammenfassung spontansprachlicher Interaktionen besser geeignet sind als extraktive
Argumentative zoning information extraction from scientific text
Let me tell you, writing a thesis is not always a barrel of laughsâand strange things can happen, too. For example, at the height of my thesis paranoia, I had a re-current dream in which my cat Amy gave me detailed advice on how to restructure the thesis chapters, which was awfully nice of her. But I also had a lot of human help throughout this time, whether things were going fine or beserk. Most of all, I want to thank Marc Moens: I could not have had a better or more knowledgable supervisor. He always took time for me, however busy he might have been, reading chapters thoroughly in two days. He both had the calmness of mind to give me lots of freedom in research, and the right judgement to guide me away, tactfully but determinedly, from the occasional catastrophe or other waiting along the way. He was great fun to work with and also became a good friend. My work has profitted from the interdisciplinary, interactive and enlightened atmosphere at the Human Communication Centre and the Centre for Cognitive Science (which is now called something else). The Language Technology Group was a great place to work in, as my research was grounded in practical applications develope
Proceedings
Proceedings of the Ninth International Workshop
on Treebanks and Linguistic Theories.
Editors: Markus Dickinson, Kaili MĂŒĂŒrisep and Marco Passarotti.
NEALT Proceedings Series, Vol. 9 (2010), 268 pages.
© 2010 The editors and contributors.
Published by
Northern European Association for Language
Technology (NEALT)
http://omilia.uio.no/nealt .
Electronically published at
Tartu University Library (Estonia)
http://hdl.handle.net/10062/15891
Meeting decision detection: multimodal information fusion for multi-party dialogue understanding
Modern advances in multimedia and storage technologies have led to huge archives
of human conversations in widely ranging areas. These archives offer a wealth of information
in the organization contexts. However, retrieving and managing information
in these archives is a time-consuming and labor-intensive task. Previous research applied
keyword and computer vision-based methods to do this. However, spontaneous
conversations, complex in the use of multimodal cues and intricate in the interactions
between multiple speakers, have posed new challenges to these methods. We need
new techniques that can leverage the information hidden in multiple communication
modalities â including not just âwhatâ the speakers say but also âhowâ they express
themselves and interact with others.
In responding to this need, the thesis inquires into the multimodal nature of meeting
dialogues and computational means to retrieve and manage the recorded meeting
information. In particular, this thesis develops the Meeting Decision Detector (MDD)
to detect and track decisions, one of the most important outcomes of the meetings.
The MDD involves not only the generation of extractive summaries pertaining to the
decisions (âdecision detectionâ), but also the organization of a continuous stream of
meeting speech into locally coherent segments (âdiscourse segmentationâ).
This inquiry starts with a corpus analysis which constitutes a comprehensive empirical
study of the decision-indicative and segment-signalling cues in the meeting
corpora. These cues are uncovered from a variety of communication modalities, including
the words spoken, gesture and head movements, pitch and energy level, rate
of speech, pauses, and use of subjective terms. While some of the cues match the
previous findings of speech segmentation, some others have not been studied before.
The analysis also provides empirical grounding for computing features and integrating
them into a computational model. To handle the high-dimensional multimodal
feature space in the meeting domain, this thesis compares empirically feature discriminability
and feature pattern finding criteria. As the different knowledge sources are
expected to capture different types of features, the thesis also experiments with methods
that can harness synergy between the multiple knowledge sources.
The problem formalization and the modeling algorithm so far correspond to an
optimal setting: an off-line, post-meeting analysis scenario. However, ultimately the
MDD is expected to be operated online â right after a meeting, or when a meeting
is still in progress. Thus this thesis also explores techniques that help relax the optimal
setting, especially those using only features that can be generated with a higher
degree of automation. Empirically motivated experiments are designed to handle the
corresponding performance degradation.
Finally, with the users in mind, this thesis evaluates the use of query-focused summaries
in a decision debriefing task, which is common in the organization context. The
decision-focused extracts (which represent compressions of 1%) is compared against
the general-purpose extractive summaries (which represent compressions of 10-40%).
To examine the effect of model automation on the debriefing task, this evaluation experiments
with three versions of decision-focused extracts, each relaxing one manual
annotation constraint. Task performance is measured in actual task effectiveness, usergenerated
report quality, and user-perceived success. The usersâ clicking behaviors are
also recorded and analyzed to understand how the users leverage the different versions
of extractive summaries to produce abstractive summaries.
The analysis framework and computational means developed in this work is expected
to be useful for the creation of other dialogue understanding applications, especially
those that require to uncover the implicit semantics of meeting dialogues
The significance of silence. Long gaps attenuate the preference for âyesâ responses in conversation.
In conversation, negative responses to invitations, requests, offers and the like more often occur with a delay â conversation analysts talk of them as dispreferred. Here we examine the contrastive cognitive load âyesâ and ânoâ responses make, either when given relatively fast (300 ms) or delayed (1000 ms). Participants heard minidialogues, with turns extracted from a spoken corpus, while having their EEG recorded. We find that a fast ânoâ evokes an N400-effect relative to a fast âyesâ, however this contrast is not present for delayed responses. This shows that an immediate response is expected to be positive â but this expectation disappears as the response time lengthens because now in ordinary conversation the probability of a ânoâ has increased. Additionally, however, 'No' responses elicit a late frontal positivity both when they are fast and when they are delayed. Thus, regardless of the latency of response, a ânoâ response is associated with a late positivity, since a negative response is always dispreferred and may require an account. Together these results show that negative responses to social actions exact a higher cognitive load, but especially when least expected, as an immediate response
EVALITA Evaluation of NLP and Speech Tools for Italian Proceedings of the Final Workshop
Editor of the proceedings of EVALITA 2016
Proceedings of the Conference on Natural Language Processing 2010
This book contains state-of-the-art contributions to the 10th
conference on Natural Language Processing, KONVENS 2010
(Konferenz zur Verarbeitung natĂŒrlicher Sprache), with a focus
on semantic processing.
The KONVENS in general aims at offering a broad perspective
on current research and developments within the interdisciplinary
field of natural language processing. The central theme
draws specific attention towards addressing linguistic aspects
ofmeaning, covering deep as well as shallow approaches to semantic
processing. The contributions address both knowledgebased
and data-driven methods for modelling and acquiring
semantic information, and discuss the role of semantic information
in applications of language technology.
The articles demonstrate the importance of semantic processing,
and present novel and creative approaches to natural
language processing in general. Some contributions put their
focus on developing and improving NLP systems for tasks like
Named Entity Recognition or Word Sense Disambiguation, or
focus on semantic knowledge acquisition and exploitation with
respect to collaboratively built ressources, or harvesting semantic
information in virtual games. Others are set within the
context of real-world applications, such as Authoring Aids, Text
Summarisation and Information Retrieval. The collection highlights
the importance of semantic processing for different areas
and applications in Natural Language Processing, and provides
the reader with an overview of current research in this field