Abstract
In today’s fast-paced world, users face the challenge of having to consume a lot of content in a short time. This situation is exacerbated by the fact that content is scattered in a range of different languages and locations. This research addresses these challenges using a number of natural language processing techniques: adapting content using automatic text summarization; enhancing content accessibility through machine translation; and altering the delivery modality through speech synthesis. This paper introduces Lean-back Learning (LbL), an information system that delivers automatically generated audio presentations for consumption in a “lean-back” fashion, i.e. hands-busy, eyes-busy situations. These presentations are personalized and are generated using multilingual multi-document text summarization. The paper discusses the system’s components and algorithms, in addition to initial system evaluations.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
The MT service used in the experiments is Bing Translation API. The following are the reasons for choosing Bing: (a) it is generally known to perform relatively well in terms of translation quality and speed; (b) it supports a range of languages; and (c) it provides a well-defined RESTful Web service to communicate with it.
References
Murray, G., Renals, S., Carletta, J.: Extractive summarization of meeting recordings. In: Proceedings, Interspeech’ 2005 - Eurospeech, 9th European Conference on Speech Communication and Technology. Lisbon, Portugal (2005)
Fiszman, M., Rindflesch, T.C.: Abstraction summarization for managing the biomedical research literature. In: Proceedings of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (HLT-NAACL) Workshop on Computational Lexical Semantics (CLS), pp. 76–83, Boston, Massachusetts (2004)
Vodolazova, T., Lloret, E., Muñoz, R., Palomar, M.: A comparative study of the impact of statistical and semantic features in the framework of extractive text summarization. In: 15th International Conference on Text, Speech Dialogue, (TSD), pp. 306–313, (2012)
Nenkova, A., Mckeown, K.R.: Automatic summarization. Found. Trends Inf. Retrieval 5, 103–233 (2011)
Radev, D.R.: LexRank: graph-based lexical centrality as salience in text summarization. J. Artif. Intell. Res. 22, 457–479 (2004)
Mihalcea, R., Tarau, P.: TextRank: Bringing order into texts. In: Lin D., Wu D. (eds) Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Barcelona, Spain, pp. 404–411 (2004)
Edmundson, H.P.: New methods in automatic extracting. J. ACM 16(2), 264–285 (1969)
Teufel, S., Moens, M.: Sentence extraction as a classification task. In: ACL/EACL workshop on Intelligent and scalable Text summarization, pp. 58–65, Madrid, Spain (1997)
Luhn, H.P.: The automatic creation of literature abstracts. IBM J. Res. Dev. 2(2), 159–165 (1958)
Black, A., Zen, H., Tokuda, K.: Statistical parametric speech synthesis. In: Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2007), pp. 1229–1232 (2007)
Ling, Z., Wang, R.: HMM-based hierarchical unit selection combining kullback-leibler divergence with likelihood criterion. In: Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2007), pp. 1245–1248 (2007)
Türk, O., Schröder, M.: Evaluation of expressive speech synthesis with voice conversion and copy resynthesis techniques. IEEE Trans. Audio, Speech, Lang. Proc. 18(5), 965–973 (2010)
Székely, E., Cabral, J.P., Cahill, P., Carson-Berndsen, J.: Clustering expressive speech styles in audiobooks using glottal source parameters. In: Proceedings of Interspeech, Florence, Italy (2011)
Yamagishi, J., Kobayashi, T.: Adaptive training for hidden semi-Markov model. In: Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2005), Philadelphia, USA (2005)
Tokuda, K., Zen, H., Yamagishi, J., Black, A., Masuko, T., Sako, S.: The HMM-based speech synthesis system (HTS), version 2.1 (2009). http://hts.sp.nitech.ac.jp/
Kominek, J., Black, A.: The CMU arctic speech databases. In: Proceedings of 5th ISCA Speech Synthesis Workshop (SSW5), Pittsburgh, USA (2004)
Clark, R., Richmond, K., King, S.: Multisyn: Open-domain unit selection for the Festival speech synthesis system. Speech Commun. 49, 317–330 (2007)
Kawahara, H., Masuda-Katsuse, I., Cheveigné, A.: Restructuring speech representations using a pitch-adaptive time-frequency smoothing and an instantaneous-frequency-based F0 extraction: possible role of a repetitive structure in sounds. Speech Commun. 27, 187–207 (1999)
Talkin, D.: A robust algorithm for pitch tracking (RAPT). In: Kleijn W.B., Paliwal K.K. (eds.) Speech Coding and Synthesis, pp. 495–518. Elsevier Science, New York (1995)
Schröder, M., Trouvain, J.: The German text-to-speech synthesis system Mary: a tool for research, development and teaching. Int. J. Speech Technol. 6, 365–377 (2003)
Steinberger, J., Ježek, K.: Evaluation measures for text summarization. Comput. Inf. 28(2), 1001–1026 (2012)
Lin, C., Rey, M.: ROUGE: a package for automatic evaluation of summaries. In: Proceedings of Workshop on Text Summarization Branches Out, Post-Conference Workshop of ACL 2004, Barcelona, Spain (2004)
Augat, M., Ladlow, M.: An NLTK Package for Lexical-Chain Based Word Sense Disambiguation (2009)
Tofiloski, M., Julian, B., Maite, T.: A syntactic and lexical-based discourse segmenter. In: Proceedings of the Joint Conference of the 47th Annual Meeting of the Association for Computational Linguistics and the 4th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing (ACL-IJCNLP 2009) - Short Papers. Association for Computational Linguistics, (2009)
Zen, H., Toda, T.: An overview of Nitech HMM-based speech synthesis system for blizzard challenge 2005. In: Blizzard Challenge Workshop, Lisbon, Portugal (2005)
King, S., Karaiskos, V.: The Blizzard Challenge 2013. In: Blizzard Challenge Workshop. Barcelona, Spain (2013)
Schröder, M., Pammi, S., Türk, O.: Multilingual MARY TTS participation in the Blizzard Challenge 2009. In: Blizzard Challenge Workshop, Edinburgh, UK (2009)
Acknowledgements
This research is supported by the Science Foundation Ireland (grant 07/CE/I1142) as part of the Centre for Next Generation Localisation (www.cngl.ie) at Trinity College, Dublin.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Lawless, S., Lavin, P., Bayomi, M., Cabral, J.P., Ghorab, M.R. (2015). Text Summarization and Speech Synthesis for the Automated Generation of Personalized Audio Presentations. In: Biemann, C., Handschuh, S., Freitas, A., Meziane, F., Métais, E. (eds) Natural Language Processing and Information Systems. NLDB 2015. Lecture Notes in Computer Science(), vol 9103. Springer, Cham. https://doi.org/10.1007/978-3-319-19581-0_28
Download citation
DOI: https://doi.org/10.1007/978-3-319-19581-0_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-19580-3
Online ISBN: 978-3-319-19581-0
eBook Packages: Computer ScienceComputer Science (R0)