Publications by Gabriel Murray

s0345701.bib

@article{murray2009,
  author = {Murray, Gabriel and Kleinbauer, Thomas and Poller,
                   Peter and Becker, Tilman and Renals, Steve and Kilgour,
                   Jonathan},
  title = {Extrinsic Summarization Evaluation: A Decision Audit
                   Task},
  journal = {ACM Transactions on Speech and Language Processing},
  volume = {6},
  number = {2},
  pages = {1--29},
  abstract = {In this work we describe a large-scale extrinsic
                   evaluation of automatic speech summarization
                   technologies for meeting speech. The particular task is
                   a decision audit, wherein a user must satisfy a complex
                   information need, navigating several meetings in order
                   to gain an understanding of how and why a given
                   decision was made. We compare the usefulness of
                   extractive and abstractive technologies in satisfying
                   this information need, and assess the impact of
                   automatic speech recognition (ASR) errors on user
                   performance. We employ several evaluation methods for
                   participant performance, including post-questionnaire
                   data, human subjective and objective judgments, and a
                   detailed analysis of participant browsing behavior. We
                   find that while ASR errors affect user satisfaction on
                   an information retrieval task, users can adapt their
                   browsing behavior to complete the task satisfactorily.
                   Results also indicate that users consider extractive
                   summaries to be intuitive and useful tools for browsing
                   multimodal meeting data. We discuss areas in which
                   automatic summarization techniques can be improved in
                   comparison with gold-standard meeting abstracts.},
  doi = {10.1145/1596517.1596518},
  pdf = {http://www.cstr.inf.ed.ac.uk/downloads/publications/2009/murray-acm09.pdf},
  url = {http://doi.acm.org/10.1145/1596517.1596518},
  year = 2009
}

@incollection{murray2008c,
  author = {Murray, Gabriel and Kleinbauer, Thomas and Poller,
                   Peter and Renals, Steve and Kilgour, Jonathan},
  title = {Extrinsic Summarization Evaluation: A Decision Audit
                   Task},
  booktitle = {Machine Learning for Multimodal Interaction (Proc.
                   MLMI '08)},
  publisher = {Springer},
  number = {5237},
  series = {Lecture Notes in Computer Science},
  pages = {349--361},
  abstract = {In this work we describe a large-scale extrinsic
                   evaluation of automatic speech summarization
                   technologies for meeting speech. The particular task is
                   a decision audit, wherein a user must satisfy a complex
                   information need, navigating several meetings in order
                   to gain an understanding of how and why a given
                   decision was made. We compare the usefulness of
                   extractive and abstractive technologies in satisfying
                   this information need, and assess the impact of
                   automatic speech recognition (ASR) errors on user
                   performance. We employ several evaluation methods for
                   participant performance, including post-questionnaire
                   data, human subjective and objective judgments, and an
                   analysis of participant browsing behaviour.},
  doi = {10.1007/978-3-540-85853-9_32},
  pdf = {http://www.cstr.inf.ed.ac.uk/downloads/publications/2008/murray2008c.pdf},
  year = 2008
}

@incollection{murray2008b,
  author = {Murray, Gabriel and Renals, Steve},
  title = {Detecting Action Items in Meetings},
  booktitle = {Machine Learning for Multimodal Interaction (Proc.
                   MLMI '08)},
  publisher = {Springer},
  number = {5237},
  series = {Lecture Notes in Computer Science},
  pages = {208--213},
  abstract = {We present a method for detecting action items in
                   spontaneous meeting speech. Using a supervised approach
                   incorporating prosodic, lexical and structural
                   features, we can classify such items with a high degree
                   of accuracy. We also examine how well various feature
                   subclasses can perform this task on their own.},
  doi = {10.1007/978-3-540-85853-9_19},
  pdf = {http://www.cstr.inf.ed.ac.uk/downloads/publications/2008/murray2008b.pdf},
  url = {http://dx.doi.org/10.1007/978-3-540-85853-9_19},
  year = 2008
}

@inproceedings{Hachey05,
  author = {B. Hachey and G. Murray and D. Reitter},
  title = {The {E}mbra System at {DUC} 2005: Query-oriented
                   Multi-document Summarization with a Very Large Latent
                   Semantic Space},
  booktitle = {Proceedings of the Document Understanding Conference
                   (DUC) 2005, Vancouver, BC, Canada},
  abstract = {Our summarization system submitted to DUC 2005, Embra
                   (or Edinburgh), is novel in that it relies on building
                   a very large semantic space for the purposes of
                   determining relevance and redundancy in an MMR-style
                   framework. We address specificity by detecting the
                   presence or absence of Named Entities in our extract
                   candidates, and we implemented a sentence-ordering
                   algorithm to maximize sentence cohesion in our final
                   summaries.},
  categories = {summarization, latent semantic analysis},
  month = oct,
  pdf = {http://www.cstr.inf.ed.ac.uk/downloads/publications/2005/duc2005.pdf},
  year = 2005
}

@inproceedings{murray06,
  author = {G. Murray and S. Renals and J. Moore and J. Carletta},
  title = {Incorporating Speaker and Discourse Features into
                   Speech Summarization},
  booktitle = {Proceedings of the Human Language Technology
                   Conference - North American Chapter of the Association
                   for Computational Linguistics Meeting (HLT-NAACL) 2006,
                   New York City, USA},
  abstract = {The research presented herein explores the usefulness
                   of incorporating speaker and discourse features in an
                   automatic speech summarization system applied to
                   meeting recordings from the ICSI Meetings corpus. By
                   analyzing speaker activity, turn-taking and discourse
                   cues, it is hypothesized that a system can outperform
                   solely text-based methods inherited from the field of
                   text summarization. The summarization methods are
                   described, two evaluation methods are applied and
                   compared, and the results clearly show that utilizing
                   such features is advantageous and efficient. Even
                   simple methods relying on discourse cues and speaker
                   activity can outperform text summarization approaches.},
  categories = {summarization, speech summarization, prosody, latent
                   semantic analysis},
  month = jun,
  pdf = {http://www.cstr.inf.ed.ac.uk/downloads/publications/2006/hlt2006-final.pdf},
  year = 2006
}

@inproceedings{murray2007-interspeech,
  author = {Murray, Gabriel and Renals, Steve},
  title = {Towards online speech summarization},
  booktitle = {Proc. Interspeech '07},
  abstract = {The majority of speech summarization research has
                   focused on extracting the most informative dialogue
                   acts from recorde d, archived data. However, a
                   potential use case for speech sum- marization in the
                   meetings domain is to facilitate a meeting in progress
                   by providing the participants - whether they are at
                   tend- ing in-person or remotely - with an indication of
                   the most im- portant parts of the discussion so far.
                   This requires being a ble to determine whether a
                   dialogue act is extract-worthy befor e the global
                   meeting context is available. This paper introduces a
                   novel method for weighting dialogue acts using only
                   very lim- ited local context, and shows that high
                   summary precision is possible even when information
                   about the meeting as a whole is lacking. A new
                   evaluation framework consisting of weighted precision,
                   recall and f-score is detailed, and the novel onl ine
                   summarization method is shown to significantly increase
                   recall and f-score compared with a method using no
                   contextual infor- mation. },
  pdf = {http://www.cstr.inf.ed.ac.uk/downloads/publications/2007/IS070966.PDF},
  year = 2007
}

@inproceedings{hachey06,
  author = {B. Hachey and G. Murray and D. Reitter},
  title = {Dimensionality Reduction Aids Term Co-Occurrence Based
                   Multi-Document Summarization},
  booktitle = {Proceedings of ACL Summarization Workshop 2006,
                   Sydney, Australia},
  abstract = {A key task in an extraction system for query-oriented
                   multi-document summarisation, necessary for computing
                   relevance and redundancy, is modelling text semantics.
                   In the Embra system, we use a representation derived
                   from the singular value decomposition of a term
                   co-occurrence matrix. We present methods to show the
                   reliability of performance improvements. We find that
                   Embra performs better with dimensionality reduction.},
  categories = {summarization, latent semantic analysis},
  month = jun,
  pdf = {http://www.cstr.inf.ed.ac.uk/downloads/publications/2006/coling-acl2006.pdf},
  year = 2006
}

@incollection{murray2007-mlmi,
  author = {Murray, Gabriel and Renals, Steve},
  title = {Term-weighting for summarization of multi-party spoken
                   dialogues},
  booktitle = {Machine Learning for Multimodal Interaction IV },
  publisher = {Springer},
  editor = {Popescu-Belis, A. and Renals, S. and Bourlard, H.},
  volume = {4892},
  series = {Lecture Notes in Computer Science},
  pages = {155--166},
  abstract = {This paper explores the issue of term-weighting in the
                   genre of spontaneous, multi-party spoken dialogues,
                   with the intent of using such term-weights in the
                   creation of extractive meeting summaries. The field of
                   text information retrieval has yielded many
                   term-weighting tech- niques to import for our purposes;
                   this paper implements and compares several of these,
                   namely tf.idf, Residual IDF and Gain. We propose that
                   term-weighting for multi-party dialogues can exploit
                   patterns in word us- age among participant speakers,
                   and introduce the su.idf metric as one attempt to do
                   so. Results for all metrics are reported on both manual
                   and automatic speech recognition (ASR) transcripts, and
                   on both the ICSI and AMI meeting corpora. },
  pdf = {http://www.cstr.inf.ed.ac.uk/downloads/publications/2007/48920155.pdf},
  year = 2007
}

@inproceedings{Murray05b,
  author = {G. Murray and S. Renals and J. Carletta and J. Moore},
  title = {Evaluating Automatic Summaries of Meeting Recordings},
  booktitle = {Proceedings of the 43rd Annual Meeting of the
                   Association for Computational Linguistics, Ann Arbor,
                   MI, USA},
  abstract = {The research below explores schemes for evaluating
                   automatic summaries of business meetings, using the
                   ICSI Meeting Corpus. Both automatic and subjective
                   evaluations were carried out, with a central interest
                   being whether or not the two types of evaluations
                   correlate with each other. The evaluation metrics were
                   used to compare and contrast differing approaches to
                   automatic summarization, the deterioration of summary
                   quality on ASR output versus manual transcripts, and to
                   determine whether manual extracts are rated
                   significantly higher than automatic extracts. },
  categories = {ami,summarization, speech summarization, prosody,
                   latent semantic analysis, summarization evaluation,
                   edinburgh},
  month = jun,
  pdf = {http://www.cstr.inf.ed.ac.uk/downloads/publications/2005/murray-renals-carletta-moore.pdf},
  year = 2005
}

@incollection{murray2008a,
  author = {Murray, Gabriel and Renals, Steve},
  title = {Meta Comments for Summarizing Meeting Speech},
  booktitle = {Machine Learning for Multimodal Interaction (Proc.
                   MLMI '08)},
  publisher = {Springer},
  number = {5237},
  series = {Lecture Notes in Computer Science},
  pages = {236--247},
  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.},
  doi = {10.1007/978-3-540-85853-9_22},
  pdf = {http://www.cstr.inf.ed.ac.uk/downloads/publications/2008/murray2008a.pdf},
  url = {http://dx.doi.org/10.1007/978-3-540-85853-9_22},
  year = 2008
}

@inproceedings{murray-interspeech05,
  author = {G. Murray and S. Renals and J. Carletta},
  title = {Extractive Summarization of Meeting Recordings},
  booktitle = {Proc. Interspeech},
  abstract = {Several approaches to automatic speech summarization
                   are discussed below, using the ICSI Meetings corpus. We
                   contrast feature-based approaches using prosodic and
                   lexical features with maximal marginal relevance and
                   latent semantic analysis approaches to summarization.
                   While the latter two techniques are borrowed directly
                   from the field of text summarization, feature-based
                   approaches using prosodic information are able to
                   utilize characteristics unique to speech data. We also
                   investigate how the summarization results might
                   deteriorate when carried out on ASR output as opposed
                   to manual transcripts. All of the summaries are of an
                   extractive variety, and are compared using the software
                   ROUGE.},
  categories = {ami,summarization,prosody, latent semantic
                   analysis,edinburgh},
  month = sep,
  pdf = {http://www.cstr.inf.ed.ac.uk/downloads/publications/2005/murray-eurospeech05.pdf},
  year = 2005
}

@inproceedings{murray06b,
  author = {G. Murray and S. Renals and M. Taboada},
  title = {Prosodic Correlates of Rhetorical Relations},
  booktitle = {Proceedings of HLT/NAACL ACTS Workshop, 2006, New York
                   City, USA},
  abstract = {This paper investigates the usefulness of prosodic
                   features in classifying rhetorical relations between
                   utterances in meeting recordings. Five rhetorical
                   relations of \textit{contrast}, \textit{elaboration},
                   \textit{summary}, \textit{question} and \textit{cause}
                   are explored. Three training methods - supervised,
                   unsupervised, and combined - are compared, and
                   classification is carried out using support vector
                   machines. The results of this pilot study are
                   encouraging but mixed, with pairwise classification
                   achieving an average of 68\% accuracy in discerning
                   between relation pairs using only prosodic features,
                   but multi-class classification performing only slightly
                   better than chance.},
  categories = {rhetorical structure theory, prosody, unsupervised
                   learning},
  month = jun,
  pdf = {http://www.cstr.inf.ed.ac.uk/downloads/publications/2006/dacts-hlt.pdf},
  year = 2006
}

@inproceedings{murray06c,
  author = {G. Murray and S. Renals},
  title = {Dialogue Act Compression Via Pitch Contour
                   Preservation},
  booktitle = {Proceedings of the 9th International Conference on
                   Spoken Language Processing, Pittsburgh, USA},
  abstract = {This paper explores the usefulness of prosody in
                   automatically compressing dialogue acts from meeting
                   speech. Specifically, this work attempts to compress
                   utterances by preserving the pitch contour of the
                   original whole utterance. Two methods of doing this are
                   described in detail and are evaluated
                   \textit{subjectively} using human annotators and
                   \textit{objectively} using edit distance with a
                   human-authored gold-standard. Both metrics show that
                   such a prosodic approach is much better than the random
                   baseline approach and significantly better than a
                   simple text compression method.},
  categories = {automatic compression, prosody, summarization},
  month = sep,
  pdf = {http://www.cstr.inf.ed.ac.uk/downloads/publications/2006/inter2006.pdf},
  year = 2006
}