Hostname: page-component-78c5997874-j824f Total loading time: 0 Render date: 2024-11-13T04:13:46.280Z Has data issue: false hasContentIssue false
  • English
  • Français

Extractive summarization of multi-party meetings through discourse segmentation

Published online by Cambridge University Press:  04 March 2015

MOHAMMAD HADI BOKAEI ,
HOSSEIN SAMETI  and
YANG LIU
MOHAMMAD HADI BOKAEI
Affiliation:
Speech Processing Lab, Computer Engineering Department, Sharif University of Technology, Tehran, I.R. Iran e-mail: bokaei@ce.sharif.edu, sameti@sharif.edu Human Language Technology Group, Computer Science Department, The University of Texas at Dallas, Richardson, TX, USA e-mail: yangl@hlt.utdallas.edu
HOSSEIN SAMETI
Affiliation:
Speech Processing Lab, Computer Engineering Department, Sharif University of Technology, Tehran, I.R. Iran e-mail: bokaei@ce.sharif.edu, sameti@sharif.edu
YANG LIU
Affiliation:
Human Language Technology Group, Computer Science Department, The University of Texas at Dallas, Richardson, TX, USA e-mail: yangl@hlt.utdallas.edu
Get access Rights & Permissions [Opens in a new window]

Abstract

In this article we tackle the problem of multi-party conversation summarization. We investigate the role of discourse segmentation of a conversation on meeting summarization. First, an unsupervised function segmentation algorithm is proposed to segment the transcript into functionally coherent parts, such as Monologuei (which indicates a segment where speaker i is the dominant speaker, e.g., lecturing all the other participants) or Discussionx1x2, . . ., xn (which indicates a segment where speakers x1 to xn involve in a discussion). Then the salience score for a sentence is computed by leveraging the score of the segment containing the sentence. Performance of our proposed segmentation and summarization algorithms is evaluated using the AMI meeting corpus. We show better summarization performance over other state-of-the-art algorithms according to different metrics.

Type
Articles
Information
Natural Language Engineering , Volume 22 , Issue 1 , January 2016 , pp. 41 - 72
Copyright
Copyright © Cambridge University Press 2015 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Angheluta, R., De Busser, R., and Moens, M.-F., 2002. The use of topic segmentation for automatic summarization. In Proceedings of the ACL-2002 Workshop on Automatic Summarization, New Brunswick, NJ: ACL, pp. 66–70.Google Scholar
Banerjee, S., and Rudnicky, A. I., 2008. An extractive-summarization baseline for the automatic detection of noteworthy utterances in multi-party human-human dialog. In Proceedings of the Spoken Language Technology Workshop, Goa, India: IEEE, pp. 177–180.Google Scholar
Beeferman, D., Berger, A., and Lafferty, J. 1999. Statistical models for text segmentation. Machine Learning 34 (1–3): 177–210. Springer.CrossRefGoogle Scholar
Blei, D. M., Ng, A. Y., and Jordan, M. I. 2003. Latent dirichlet allocation. The Journal of Machine Learning Research 3: 993–1022. MIT Press.Google Scholar
Boguraev, B. K., and Neff, M. S. 2000. Discourse segmentation in aid of document summarization. In Proceedings of the 33rd Annual Hawaii International Conference on System Sciences, Island of Maui: IEEE, pp. 10–20.Google Scholar
Brin, S., and Page, L. 1998. The anatomy of a large-scale hypertextual web search engine. Computer Networks and ISDN Systems 30 (1): 107–117. Elsevier.CrossRefGoogle Scholar
Buist, A. H., Kraaij, W., and Raaijmakers, S. 2005. Automatic summarization of meeting data: a feasibility study. In Proceedings of the 15th CLIN Conference, Leiden, The Netherlands.Google Scholar
Carbonell, J., and Goldstein, J., 1998. The use of mmr, diversity-based reranking for reordering documents and producing summaries. In Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Melbourne, Australia: ACM, pp. 335–336.CrossRefGoogle Scholar
Carletta, J.et al., 2006. The ami meeting corpus: a pre-announcement. In Proceedings of Machine Learning for Multimodal Interaction, Brno, Czech Republic: Springer, pp. 28–39.CrossRefGoogle Scholar
Chen, Y.-N., and Metze, F., 2012. Two-layer mutually reinforced random walk for improved multi-party meeting summarization. In Proceedings of Spoken Language Technology Workshop, Miami, Florida: IEEE, pp. 461–466.Google Scholar
Chen, Y. N., and Metze, F., 2013. Multi-layer mutually reinforced random walk with hidden parameters for improved multi-party meeting summarization. In Proceedings of the Conference of the International Speech Communication Association(INTERSPEECH), Lyon, France, pp. 485–489.Google Scholar
Deerwester, S. C., Dumais, S. T., Landauer, T. K., Furnas, G. W., and Harshman, R. A., 1990. Indexing by latent semantic analysis. Journal of the American Society for Information Science (JASIS) 41 (6): 391–407.3.0.CO;2-9>CrossRefGoogle Scholar
Dielmann, A., and Renals, S., 2004. Dynamic bayesian networks for meeting structuring. In Proceedings of the International Conference on Acoustic, Speech, and Signal Processing (ICASSP), Montreal, Canada. vol. 5, IEEE, pp. 626–629.Google Scholar
Eiben, A., and Smith, J., 2003. Introduction to Evolutionary Computing. Berlin, Heidelberg: Springer, Springer-Verlag.CrossRefGoogle Scholar
Engelbrecht, A. P., 2007. Computational Intelligence: an Introduction. England: John Wiley & Sons Ltd.CrossRefGoogle Scholar
Erkan, G., and Radev, D. R. 2004. Lexrank: graph-based lexical centrality as salience in text summarization. Journal of Artificial Intelligence Research (JAIR) 22 (1): 457–479. AI Access Foundation.CrossRefGoogle Scholar
Fernández, R., Frampton, M., Ehlen, P., Purver, M., and Peters, S., 2008. Modelling and detecting decisions in multi-party dialogue. In Proceedings of the 9th SIGdial Workshop on Discourse and Dialogue, Columbus, OH, USA: ACL, pp. 156–163.CrossRefGoogle Scholar
Fung, P., and Ngai, G., 2006. One story, one flow: hidden markov story models for multilingual multidocument summarization. ACM Transactions on Speech and Language Processing (TSLP) 3 (2): 1–16.CrossRefGoogle Scholar
Galley, M., 2006. A skip-chain conditional random field for ranking meeting utterances by importance. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, Sydney, Australia: ACL, pp. 364–372.Google Scholar
Galley, M., McKeown, K., Fosler-Lussier, E., and Jing, H., 2003. Discourse segmentation of multi-party conversation. In Proceedings of the 41st Annual Meeting on Association for Computational Linguistics, Sapporo, Japan. vol. 1, : ACL, pp. 562–569.Google Scholar
Garg, N., Favre, B., Riedhammer, K., and Hakkani-Tür, D., 2009. Clusterrank: a graph based method for meeting summarization. In Proceedings of the Conference of the International Speech Communication Association (INTERSPEECH), Brighton, UK, pp. 1499–1502.Google Scholar
Georgescul, M., Clark, A., and Armstrong, S., 2009. An analysis of quantitative aspects in the evaluation of thematic segmentation algorithms. In Proceedings of the 7th SIGdial Workshop on Discourse and Dialogue, London, UK: ACL, pp. 144–151.Google Scholar
Germesin, S., and Wilson, T., 2009. Agreement detection in multiparty conversation. In Proceedings of the International Conference on Multimodal Interfaces, Cambridge, MA, USA: ACM, pp. 7–14.Google Scholar
Gillick, D., Riedhammer, K., Favre, B., and Hakkani-Tur, D., 2009. A global optimization framework for meeting summarization. In Proceedings of the International Conference on Acoustic, Speech, and Signal Processing (ICASSP), Taipei, Taiwan: IEEE, pp. 4769–4772.Google Scholar
Goldberg, D. E., and Richardson, J., 1987. Genetic algorithms with sharing for multimodal function optimization. In Proceedings of the Second International Conference on Genetic Algorithms and their application, Hillsdale, NJ, USA: Lawrence Erlbaum Associates, pp. 41–49.Google Scholar
Gong, Y., and Liu, X., 2001. Generic text summarization using relevance measure and latent semantic analysis. In Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, New Orleans, Louisiana, USA: ACM, pp. 19–25.CrossRefGoogle Scholar
Grosz, B. J., and Sidner, C. L. 1986. Attention, intentions, and the structure of discourse. Computational Linguistics 12 (3): 175–204. MIT Press.Google Scholar
Hearst, M. A. 1997. Texttiling: segmenting text into multi-paragraph subtopic passages. Computational Linguistics 23 (1): 33–64. MIT Press.Google Scholar
Hillard, D., Ostendorf, M., and Shriberg, E., 2003. Detection of agreement vs. disagreement in meetings: Training with unlabeled data. In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology: Companion Volume of the Proceedings of HLT-NAACL 2003–short papers-Volume 2, Edmonton, Canada: ACL, pp. 34–36.Google Scholar
Hirohata, K., Okazaki, N., Ananiadou, S., Ishizuka, M., and Biocentre, M. I., 2008. Identifying sections in scientific abstracts using conditional random fields. In Proceedings of the International Joint Conference on Natural Language Processing (IJCNLP), Hyderabad, India, pp. 381–388.Google Scholar
Hirschberg, J., and Litman, D. 1993. Empirical studies on the disambiguation of cue phrases. Computational Linguistics 19 (3): 501–530. MIT Press.Google Scholar
Hofmann, T., 1999. Probabilistic latent semantic indexing. In Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Berkeley, USA: ACM, pp. 50–57.CrossRefGoogle Scholar
Holland, J. H. 1975. Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. The University of Michigan Press.Google Scholar
Hsueh, P. Y., Moore, J. D., and Renals, S., 2006. Automatic segmentation of multiparty dialogue. In Proceedings of the 11th Conference of the European Chapter of the Association for Computational Linguistics, Trento, Italy: ACL, pp. 273–280.Google Scholar
Joty, S. R., Carenini, G., and Ng, R. T. 2014. Topic segmentation and labeling in asynchronous conversations. Journal of Artificial Intelligence Research 47: 521–573. AI Access Foundation.CrossRefGoogle Scholar
Kong, S.-Y., and Lee, L.-S., 2006. Improved spoken document summarization using probabilistic latent semantic analysis (plsa). In Proceedings of the International Conference on Acoustic, Speech, and Signal Processing (ICASSP), Toulouse, France: IEEE, vol. 1, pp. 941–944.Google Scholar
Kullback, S., and Leibler, R. A. 1951. On information and sufficiency. The Annals of Mathematical Statistics 22 (1): 79–86. Institute of Mathematical Statistics.CrossRefGoogle Scholar
Lamprier, S., Amghar, T., Levrat, B., and Saubion, F., 2007. SegGen: a genetic algorithm for linear text segmentation. In Proceedings of the 20th International Joint Conferences on Artificial Intelligence (IJCAI), Hyderabad, India. vol. 7, pp. 1647–1652.Google Scholar
Lascarides, A., and Asher, N. 1993. Temporal interpretation, discourse relations and commonsense entailment. Linguistics and Philosophy 16 (5): 437–493. SpringerCrossRefGoogle Scholar
Levenshtein, V. I., 1966. Binary codes capable of correcting deletions, insertions and reversals. Soviet Physics Doklady 10: 707.Google Scholar
Lin, C.-Y. 2004. Rouge: a package for automatic evaluation of summaries. In Proceedings of the ACL Workshop, Barcelona, Spain pp. 74–81.Google Scholar
Liu, F., and Liu, Y., 2008. Correlation between rouge and human evaluation of extractive meeting summaries. In Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies, Columbus, OH, USA: ACL, pp. 201–204.Google Scholar
Liu, F., and Liu, Y., 2010. Exploring speaker characteristics for meeting summarization. In Proceedings of the Conference of the International Speech Communication Association (INTERSPEECH), Makuhari, Japan, pp. 2518–2521.Google Scholar
Liu, Y., and Hakkani-Tür, D. 2011. Speech summarization. In Spoken Language Understanding: Systems for Extracting Semantic Information from Speech, Wiley, pp. 357–396.Google Scholar
Liu, Y., Shriberg, E., Stolcke, A., Hillard, D., Ostendorf, M., and Harper, M. 2006. Enriching speech recognition with automatic detection of sentence boundaries and disfluencies. IEEE Transactions on Audio, Speech, and Language Processing 14 (5): 1526–1540. IEEE.CrossRefGoogle Scholar
Luhn, H. P. 1958. The automatic creation of literature abstracts. IBM Journal of Research and Development 2 (2): 159–165. IEEE.CrossRefGoogle Scholar
Malioutov, I., and Barzilay, R., 2006. Minimum cut model for spoken lecture segmentation. In Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual Meeting of the Association for Computational Linguistics, Sydney, Australia: ACL, pp. 25–32.Google Scholar
Mani, I., Klein, G., House, D., Hirschman, L., Firmin, T., and Sundheim, B. 2002. Summac: a text summarization evaluation. Natural Language Engineering 8 (1): 43–68. Cambridge University Press.CrossRefGoogle Scholar
Mani, I., and Maybury, M. T. 1999. Advances in Automatic Text Summarization. Cambridge, MA: MIT Press.Google Scholar
Mann, W. C., and Thompson, S. A. 1988. Rhetorical structure theory: toward a functional theory of text organization. Text Journal 8 (3): 243–281. Australasian Association of Writing Programs.CrossRefGoogle Scholar
Marcu, D., 1998. Improving summarization through rhetorical parsing tuning. In Proceedings of the 6th Workshop on Very Large Corpora, Montreal, Canada: ACL, pp. 206–215.Google Scholar
Maskey, S., and Hirschberg, J., 2003. Automatic summarization of broadcast news using structural features. In Proceedings of the Conference of the International Speech Communication Association (INTERSPEECH), Geneva, Switzerland, pp. 1173–1176.Google Scholar
Maskey, S., and Hirschberg, J., 2005. Comparing lexical, acoustic/prosodic, structural and discourse features for speech summarization. In Proceedings of the Conference of the International Speech Communication Association (INTERSPEECH), Lisbon, Portugal, pp. 621–624.Google Scholar
Maskey, S., and Hirschberg, J., 2006. Summarizing speech without text using hidden markov models. In Proceedings of the Human Language Technology Conference of the NAACL, New York city, USA: ACL, pp. 89–92.Google Scholar
McCowan, I., Bengio, S., Gatica-Perez, D., Lathoud, G., Monay, F., Moore, D., Wellner, P., and Bourlard, H., 2003. Modeling human interaction in meetings. In Proceedings of the International Conference on Acoustic, Speech, and Signal Processing (ICASSP), Hong Kong; Hong Kong: IEEE, vol. 4, pp. 748–751.Google Scholar
McKnight, L., and Srinivasan, P. 2003. Categorization of sentence types in medical abstracts. In Proceedings of the AMIA Annual Symposium, American Medical Informatics Association, pp. 440–444.Google Scholar
Mehdad, Y., Carenini, G., Tompa, F. W., and NG, R. T., 2013. Abstractive meeting summarization with entailment and fusion. In Proceedings of the 14th European Workshop on Natural Language Generation, Sofia, Bulgaria: ACL, pp. 136–146.Google Scholar
Morgan, W., Chang, P.-C., Gupta, S., and Brenier, J. M., 2006. Automatically detecting action items in audio meeting recordings. In Proceedings of the 7th SIGdial Workshop on Discourse and Dialogue, Sydney, Australia ACL, pp. 96–103.CrossRefGoogle Scholar
Murray, G., Renals, S., Carletta, J., and Moore, J., 2005. Evaluating automatic summaries of meeting recordings. In Proceedings of the ACL MTSE Workshop, Ann Arbor, MI, USA, pp. 33–40.Google Scholar
Murray, G., Renals, S., Carletta, J., and Moore, J., 2006. Incorporating speaker and discourse features into speech summarization. In Proceedings of the Main Conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics, New York City, USA: ACL, pp. 367–374.Google Scholar
Murray, G., and Carenini, G., 2008. Summarizing spoken and written conversations. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, Honolulu, Hawaii. ACL, pp. 773–782.Google Scholar
Nenkova, A., and McKeown, K. 2012. A survey of text summarization techniques. In Mining Text Data, Springer, pp. 43–76.Google Scholar
Neto, J. L., Santos, A. D., Kaestner, C. A., and Freitas, A. A. 2000. Generating text summaries through the relative importance of topics. In Advances in Artificial Intelligence, Springer, pp. 300–309.Google Scholar
Oya, T., Mehdad, Y., Carenini, G., and Ng, R. T. 2014. A template-based abstractive meeting summarization: Leveraging summary and source text relationships. In Proceedings of the 8th International Natural Language Generation Conference, Philadelphia, PA, USA. pp. 35–44.Google Scholar
Pevzner, L., and Hearst, M. A. 2002. A critique and improvement of an evaluation metric for text segmentation. Computational Linguistics 28 (1): 19–36. MIT Press.CrossRefGoogle Scholar
Porter, M. F. 1980. An algorithm for suffix stripping. Program: Electronic Library and Information Systems 14 (3): 130–137. Emerald Group Publishing.CrossRefGoogle Scholar
Purver, M. 2011. Topic segmentation. In Spoken Language Understanding: Systems for Extracting Semantic Information from Speech, Wiley, pp. 291–317.Google Scholar
Purver, M., Dowding, J., Niekrasz, J., Ehlen, P., Noorbaloochi, S., and Peters, S., 2007. Detecting and summarizing action items in multi-party dialogue. In Proceedings of the 8th SIGdial Workshop on Discourse and Dialogue, Antwerp, Belgium: ACL, pp. 200–211.Google Scholar
Purver, M., Griffiths, T. L., Körding, K. P., and Tenenbaum, J. B., 2006. Unsupervised topic modelling for multi-party spoken discourse. In Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual Meeting of the Association for Computational Linguistics, Sydney, Australia: ACL, pp. 17–24.Google Scholar
Raaijmakers, S., Truong, K., and Wilson, T., 2008. Multimodal subjectivity analysis of multiparty conversation. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, Honolulu, Hawaii: ACL, pp. 466–474.Google Scholar
Ramezani, M., and Feizi-Derakhshi, M. R. 2014. Automated text summarization: an overview. Applied Artificial Intelligence 28 (2): 178–215. Taylor and Francis.CrossRefGoogle Scholar
Reiter, S., and Rigoll, G., 2004. Segmentation and classification of meeting events using multiple classifier fusion and dynamic programming. In Proceedings of the 17th International Conference on Pattern Recognition (ICPR), Cambridge, UK: IEEE, vol. 3, pp. 434–437.Google Scholar
Reiter, S., Schuller, B., and Rigoll, G., 2006. A combined lstm-rnn-hmm-approach for meeting event segmentation and recognition. In Proceedings of the International Conference on Acoustic, Speech, and Signal Processing (ICASSP), Toulouse, France: IEEE, vol. 2, pp. 393–396.Google Scholar
Reiter, S., Schuller, B., and Rigoll, G., 2007. Hidden conditional random fields for meeting segmentation. In Proceedings of the International Conference on Multimedia and Expo (ICME), Beijing, China: IEEE, pp. 639–642.Google Scholar
Reynar, J. C. 1994. An automatic method of finding topic boundaries. In Proceedings of the 32nd Annual Meeting on Association for Computational Linguistics, ACL, pp. 331–333.Google Scholar
Riedhammer, K., Favre, B., and Hakkani-Tür, D. 2010. Long story short–global unsupervised models for keyphrase based meeting summarization. Speech Communication 52 (10): 801–815. Elsevier.CrossRefGoogle Scholar
Salton, G., Singhal, A., Mitra, M., and Buckley, C. 1997. Automatic text structuring and summarization. Information Processing and Management 33 (2): 193–207. Elsevier.CrossRefGoogle Scholar
Somasundaran, S., Ruppenhofer, J., and Wiebe, J., 2007. Detecting arguing and sentiment in meetings. In Proceedings of the 8th SIGdial Workshop on Discourse and Dialogue, Antwerp, Belgium: ACL, pp. 26–34.Google Scholar
Wang, L., and Cardie, C., 2012. Focused meeting summarization via unsupervised relation extraction. In Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue, Seoul, South Korea: ACL, pp. 304–313.Google Scholar
Webber, B., Egg, M., and Kordoni, V. 2012. Discourse structure and language technology. Natural Language Engineering 18 (4): 437–490. Cambridge University Press.CrossRefGoogle Scholar
Xie, S., Favre, B., Hakkani-Tür, D., and Liu, Y., 2009. Leveraging sentence weights in a concept-based optimization framework for extractive meeting summarization. In Proceedings of 10th Conference of the International Speech Communication Association (INTERSPEECH), Brighton, UK, pp. 1503–1506.Google Scholar
Xie, S., and Liu, Y., 2008. Using corpus and knowledge-based similarity measure in maximum marginal relevance for meeting summarization. In Proceedings of the International Conference on Acoustic, Speech, and Signal Processing (ICASSP), Las Vegas, NV: IEEE, pp. 4985–4988.Google Scholar
Xie, S., and Liu, Y. 2010. Improving supervised learning for meeting summarization using sampling and regression. Computer Speech and Language 24 (3): 495–514. Elsevier.CrossRefGoogle Scholar
Xie, S., Liu, Y., and Lin, H., 2008. Evaluating the effectiveness of features and sampling in extractive meeting summarization. In Spoken Language Technology Workshop, Goa, India: IEEE, pp. 157–160.Google Scholar
Zechner, K. 2002a. Automatic summarization of open-domain multiparty dialogues in diverse genres. Computational Linguistics 28 (4): 447–485. MIT Press.CrossRefGoogle Scholar
Zhang, D., Gatica-Perez, D., and Bengio, S., 2005. Semi-supervised meeting event recognition with adapted HMMs. In IEEE International Conference on Multimedia and Expo, Amsterdam, The Netherlands: IEEE, pp. 1102–1105.Google Scholar
Zhang, D., Gatica-Perez, D., Bengio, S., McCowan, I., and Lathoud, G., 2004. Multimodal group action clustering in meetings. In Proceedings of the ACM 2nd International Workshop on Video Surveillance and Sensor Networks, New York, NY, USA: ACM, pp. 54–62.CrossRefGoogle Scholar
Zhang, J., Chan, R. H. Y., and Fung, P., 2007. Improving lecture speech summarization using rhetorical information. In Proceedings of the Workshop on Automatic Speech Recognition and Understanding, Kyoto, Japan: IEEE, pp. 195–200.Google Scholar
Zhang, J., and Fung, P., 2007. Speech summarization without lexical features for mandarin broadcast news. In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics, Rochester, NY, USA: ACL, pp. 213–216.Google Scholar
Zhang, J., and Fung, P. 2012. Automatic parliamentary meeting minute generation using rhetorical structure modeling. IEEE Transactions on Audio, Speech, and Language Processing, 20 (9): 2492–2504. IEEE.CrossRefGoogle Scholar