@inproceedings{GargFavreRiedhammeretal.2009, author = {Garg, Nikhil and Favre, Benoit and Riedhammer, Korbinian and Hakkani-T{\"u}r, Dilek}, title = {A Graph Based Method for Meeting Summarization}, series = {INTERSPEECH 2009, 10th Annual Conference of the International Speech Communication Association, Brighton, United Kingdom, September 2009.}, booktitle = {INTERSPEECH 2009, 10th Annual Conference of the International Speech Communication Association, Brighton, United Kingdom, September 2009.}, pages = {1499 -- 1502}, year = {2009}, abstract = {This paper presents an unsupervised, graph based approach for extractive summarization of meetings. Graph based methods such as TextRank have been used for sentence extraction from news articles. These methods model text as a graph with sentences as nodes and edges based on word overlap. A sentence node is then ranked according to its similarity with other nodes. The spontaneous speech in meetings leads to incomplete, informed sentences with high redundancy and calls for additional measures to extract relevant sentences. We propose an extension of the TextRank algorithm that clusters the meeting utterances and uses these clusters to construct the graph. We evaluate this method on the AM I meeting corpus and show a significant improvement over TextRank and other baseline methods.}, language = {en} } @inproceedings{GillickRiedhammerFavreetal.2009, author = {Gillick, Dan and Riedhammer, Korbinian and Favre, Benoit and Hakkani-T{\"u}r, Dilek}, title = {A Global Optimization Framework for Meeting Summarization}, series = {2009 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Taipei, Taiwan, April 2009.}, booktitle = {2009 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Taipei, Taiwan, April 2009.}, pages = {4769 -- 4772}, year = {2009}, abstract = {We introduce a model for extractive meeting summarization based on the hypothesis that utterances convey bits of information, or concepts. Using keyphrases as concepts weighted by frequency, and an integer linear program to determine the best set of utterances, that is, covering as many concepts as possible while satisfying a length constraint, we achieve ROUGE scores at least as good as a ROUGE-based oracle derived from human summaries. This brings us to a critical discussion of ROUGE and the future of extractive meeting summarization.}, language = {en} } @inproceedings{RiedhammerFavreHakkaniTuer2008, author = {Riedhammer, Korbinian and Favre, Benoit and Hakkani-T{\"u}r, Dilek}, title = {A Keyphrase Based Approach to Interactive Meeting Summarization}, series = {2008 IEEE Workshop on Spoken Language Technologies (SLT), Goa, India, December 2008.}, booktitle = {2008 IEEE Workshop on Spoken Language Technologies (SLT), Goa, India, December 2008.}, pages = {153 -- 156}, year = {2008}, abstract = {Rooted in multi-document summarization, maximum marginal relevance (MMR) is a widely used algorithm for meeting summarization (MS). A major problem in extractive MS using MMR is finding a proper query: the centroid based query which is commonly used in the absence of a manually specified query, can not significantly outperform a simple baseline system. We introduce a simple yet robust algorithm to automatically extract keyphrases (KP) from a meeting which can then be used as a query in the MMR algorithm. We show that the KP based system significantly outperforms both baseline and centroid based systems. As human refined KPs show even better summarization performance, we outline how to integrate the KP approach into a graphical user interface allowing interactive summarization to match the user's needs in terms of summary length and topic focus.}, language = {en} } @inproceedings{TurStolckeVossetal.2008, author = {Tur, Gokhan and Stolcke, Andreas and Voss, Lynn and Dowding, John and Favre, Benoit and Fernandez, Raquel and Frampton, Matthew and Frandsen, Michael and Frederickson, Clive and Graciarena, Martin and Hakkani-T{\"u}r, Dilek and Kintzing, Donald and Leveque, Kyle and Mason, Shane and Niekrasz, John and Peters, Stanley and Purver, Matthew and Riedhammer, Korbinian and Shriberg, Elizabeth and Tien, Jing and Vergyri, Dimitra and Yang, Fan}, title = {The CALO Meeting Speech Recognition and Understanding System}, series = {2008 IEEE Workshop on Spoken Language Technologies (SLT), Goa, India, December 2008.}, booktitle = {2008 IEEE Workshop on Spoken Language Technologies (SLT), Goa, India, December 2008.}, pages = {69 -- 72}, year = {2008}, abstract = {The CALO meeting assistant provides for distributed meeting capture, annotation, automatic transcription and semantic analysis of multiparty meetings, and is part of the larger CALO personal assistant system. This paper summarizes the CALO-MA architecture and its speech recognition and understanding components, which include real-time and offline speech transcription, dialog act segmentation and tagging, question-answer pair identification, action item recognition, decision extraction, and summarization.}, language = {en} } @inproceedings{RiedhammerGillickFavreetal.2008, author = {Riedhammer, Korbinian and Gillick, Dan and Favre, Benoit and Hakkani-T{\"u}r, Dilek}, title = {Packing the Meeting Summarization Knapsack}, series = {INTERSPEECH 2008, 9th Annual Conference of the International Speech Communication Association (ISCA), Brisbane, Australia, September 2008.}, booktitle = {INTERSPEECH 2008, 9th Annual Conference of the International Speech Communication Association (ISCA), Brisbane, Australia, September 2008.}, pages = {2434 -- 2437}, year = {2008}, abstract = {Despite considerable work in automatic meeting summarization over the last few years, comparing results remains difficult due to varied task conditions and evaluations. To address this issue, we present a method for determining the best possible extractive summary given an evaluation metric like ROUGE. Our oracle system is based on a knapsack-packing framework, and though NP-Hard, can be solved nearly optimally by a genetic algorithm. To frame new research results in a meaningful context, we suggest presenting our oracle results alongside two simple baselines. We show oracle and baseline results for a variety of evaluation scenarios that have recently appeared in this field.}, language = {en} } @article{RiedhammerFavreHakkaniTuer2010, author = {Riedhammer, Korbinian and Favre, Benoit and Hakkani-T{\"u}r, Dilek}, title = {Long story short - Global unsupervised models for keyphrase based meeting summarization}, series = {SPEECH COMMUNICATION}, volume = {2010}, journal = {SPEECH COMMUNICATION}, number = {52(10)}, pages = {801 -- 815}, year = {2010}, language = {en} } @article{TurStolckeVossetal.2010, author = {Tur, Gokhan and Stolcke, Andreas and Voss, Lynn and Peters, Stanley and Hakkani-T{\"u}r, Dilek and Dowding, John and Favre, Benoit and Fernandez, Raquel and Frampton, Matthew and Frandsen, Michael and Frederickson, Clint and Graciarena, Martin and Kintzing, Donald and Leveque, Kyle and Mason, Shane and Niekrasz, John and Purver, Matthew and Riedhammer, Korbinian and Shriberg, Elizabeth and Tien, Jing and Vergyri, Dimitra and Yang, Fan}, title = {The CALO Meeting Assistant System}, series = {IEEE Transactions on Audio, Speech and Language Processing}, volume = {2010}, journal = {IEEE Transactions on Audio, Speech and Language Processing}, number = {18(6)}, pages = {1601 -- 1611}, year = {2010}, abstract = {The CALO Meeting Assistant (MA) provides for distributed meeting capture, annotation, automatic transcription and semantic analysis of multiparty meetings, and is part of the larger CALO personal assistant system. This paper presents the CALO-MA architecture and its speech recognition and understanding components, which include real-time and offline speech transcription, dialog act segmentation and tagging, topic identification and segmentation, question-answer pair identification, action item recognition, decision extraction, and summarization.}, language = {en} }