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105
Identifying agreement and disagreement in conversational speech: Use of Bayesian networks to model pragmatic dependencies
- In Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL
, 2004
"... We describe a statistical approach for modeling agreements and disagreements in conversational interaction. Our approach first identifies adjacency pairs using maximum entropy ranking based on a set of lexical, durational, and structural features that look both forward and backward in the discourse. ..."
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Cited by 101 (5 self)
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We describe a statistical approach for modeling agreements and disagreements in conversational interaction. Our approach first identifies adjacency pairs using maximum entropy ranking based on a set of lexical, durational, and structural features that look both forward and backward in the discourse. We then classify utterances as agreement or disagreement using these adjacency pairs and features that represent various pragmatic influences of previous agreement or disagreement on the current utterance. Our approach achieves 86.9 % accuracy, a 4.9 % increase over previous work. 1
Extractive Summarization of Meeting Recordings
- in Proceedings of the 9th European Conference on Speech Communication and Technology
, 2005
"... 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 ..."
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Cited by 97 (10 self)
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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.
A Skip-Chain Conditional Random Field for Ranking Meeting Utterances by Importance
- Association for Computational Linguistics
, 2006
"... We describe a probabilistic approach to content selection for meeting summarization. We use skipchain Conditional Random Fields (CRF) to model non-local pragmatic dependencies between paired utterances such as QUESTION-ANSWER that typically appear together in summaries, and show that these models ou ..."
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Cited by 65 (0 self)
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We describe a probabilistic approach to content selection for meeting summarization. We use skipchain Conditional Random Fields (CRF) to model non-local pragmatic dependencies between paired utterances such as QUESTION-ANSWER that typically appear together in summaries, and show that these models outperform linear-chain CRFs and Bayesian models in the task. We also discuss different approaches for ranking all utterances in a sequence using CRFs. Our best performing system achieves 91.3 % of human performance when evaluated with the Pyramid evaluation metric, which represents a 3.9 % absolute increase compared to our most competitive non-sequential classifier. 1
Incorporating speaker and discourse features into speech summarization
- In: Proc. of the HLT-NAACL 2006
, 2006
"... We have explored the usefulness of incorporating speech 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, we hypothesize that such a system can outperform sole ..."
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Cited by 38 (12 self)
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We have explored the usefulness of incorporating speech 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, we hypothesize that such 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. 1.
The ICSI Meeting Project: Resources and Research
- in Proc. of ICASSP 2004 Meeting Recognition Workshop
, 2004
"... This paper provides a progress report on ICSI’s Meeting Project, including both the data collected and annotated as part of the project, as well as the research lines such materials support. We include a general description of the official “ICSI Meeting Corpus”, as currently available through the Li ..."
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Cited by 32 (6 self)
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This paper provides a progress report on ICSI’s Meeting Project, including both the data collected and annotated as part of the project, as well as the research lines such materials support. We include a general description of the official “ICSI Meeting Corpus”, as currently available through the Linguistic Data Consortium, discuss some of the existing and planned annotations which augment the basic transcripts provided there, and describe several research efforts that make use of these materials. The corpus supports wideranging efforts, from low-level processing of the audio signal (including automatic speech transcription, speaker tracking, and work on far-field acoustics) to higher-level analyses of meeting structure, content, and interactions (such as topic and sentence segmentation, and automatic detection of dialogue acts and meeting “hot spots”). 1.
Multilevel Dialogue Act Tags
- In Proceedings of SIGDIAL'04
, 2004
"... In this paper we discuss the use of multilayered tagsets for dialogue acts, in the context of dialogue understanding for multiparty meeting recording and retrieval applications. We discuss some desiderata for such tagsets and critically examine some previous proposals. We then define MAL-TUS, a new ..."
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Cited by 31 (2 self)
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In this paper we discuss the use of multilayered tagsets for dialogue acts, in the context of dialogue understanding for multiparty meeting recording and retrieval applications. We discuss some desiderata for such tagsets and critically examine some previous proposals. We then define MAL-TUS, a new tagset based on the ICSI-MR and Switchboard tagsets, which satisfies these requirements. We present some experiments using MALTUS which attempt to compare the merits of integrated versus multi-level classifiers for the detection of dialogue acts. 1
Unsupervised approaches for automatic keyword extraction using meeting transcripts
- In NAACL’09: Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Association for Computational Linguistics
, 2009
"... This paper explores several unsupervised approaches to automatic keyword extraction using meeting transcripts. In the TFIDF (term frequency, inverse document frequency) weighting framework, we incorporated partof-speech (POS) information, word clustering, and sentence salience score. We also evaluat ..."
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Cited by 29 (2 self)
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This paper explores several unsupervised approaches to automatic keyword extraction using meeting transcripts. In the TFIDF (term frequency, inverse document frequency) weighting framework, we incorporated partof-speech (POS) information, word clustering, and sentence salience score. We also evaluated a graph-based approach that measures the importance of a word based on its connection with other sentences or words. The system performance is evaluated in different ways, including comparison to human annotated keywords using F-measure and a weighted score relative to the oracle system performance, as well as a novel alternative human evaluation. Our results have shown that the simple unsupervised TFIDF approach performs reasonably well, and the additional information from POS and sentence score helps keyword extraction. However, the graph method is less effective for this domain. Experiments were also performed using speech recognition output and we observed degradation and different patterns compared to human transcripts.
Detecting and summarizing action items in multi-party dialogue
- in Proceedings of the 8th SIGdial Workshop on Discourse and Dialogue
, 2007
"... This paper addresses the problem of identifying action items discussed in open-domain conversational speech, and does so in two stages: firstly, detecting the subdialogues in which action items are proposed, discussed and committed to; and secondly, extracting the phrases that accurately capture or ..."
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Cited by 28 (10 self)
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This paper addresses the problem of identifying action items discussed in open-domain conversational speech, and does so in two stages: firstly, detecting the subdialogues in which action items are proposed, discussed and committed to; and secondly, extracting the phrases that accurately capture or summarize the tasks they involve. While the detection problem is hard, we show that we can improve accuracy by taking account of dialogue structure. We then describe a semantic parser that identifies potential summarizing phrases, and show that for some task properties these can be more informative than plain utterance transcriptions. 1
Meeting Structure Annotation: Data and Tools
- In Proceedings of the SIGdial Workshop on Discourse and Dialogue
, 2005
"... We present a set of annotations of hierarchical topic segmentations and action item subdialogues collected over 65 meetings from the ICSI and ISL meeting corpora, designed to support automatic meeting understanding and analysis. We describe an architecture for representing, annotating, and analyzing ..."
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Cited by 25 (9 self)
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We present a set of annotations of hierarchical topic segmentations and action item subdialogues collected over 65 meetings from the ICSI and ISL meeting corpora, designed to support automatic meeting understanding and analysis. We describe an architecture for representing, annotating, and analyzing multi-party discourse, including: an ontology of multimodal discourse, a programming interface for that ontology, and an audiovisual toolkit which facilitates browsing and annotating discourse, as well as visualizing and adjusting features for machine learning tasks. 1
Using Corpus and Knowledge-Based Similarity Measure in Maximum Marginal Relevance for Meeting Summarization
- in Proc. ICASSP, Las Vegas, Nv
, 2008
"... MMR (Maximum Marginal Relevance) is widely used in summarization for its simplicity and efficacy, and has been demonstrated to achieve comparable performance to other approaches for meeting summarization. How to appropriately represent the similarity of two text segments is crucial in MMR. In this p ..."
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Cited by 20 (4 self)
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MMR (Maximum Marginal Relevance) is widely used in summarization for its simplicity and efficacy, and has been demonstrated to achieve comparable performance to other approaches for meeting summarization. How to appropriately represent the similarity of two text segments is crucial in MMR. In this paper, we evaluate different similarity measures in the MMR framework for meeting summarization on the ICSI meeting corpus. We introduce a corpusbased measure to capture the similarity at the semantic level, and compare this method with cosine similarity and centroid score that only considers the salient words in the segments. Our experimental results evaluated by the ROUGE summarization metrics show that both the centroid score and the corpus-based similarity measure yield better performance than the commonly used cosine similarity. In addition, adding part-of-speech information in the corpus-based approach helps for the human transcripts condition, but not when using ASR output. Index Terms — meeting summarization, MMR, centroid score, corpus-based similarity 1.
