Abstract
This paper describes a summarization system that was developed in order to summarize news delivered orally. The system generates text summaries from input audio using three independent components: an automatic speech recognizer, a syntactic analyzer, and a summarizer. The absence of sentence boundaries in the recognized text complicates the summarization process. Therefore, we use a syntactic analyzer to identify continuous segments in the recognized text.
We used 50 reference articles to perform our evaluation. The data are publicly available at http://nlp.ite.tul.cz/sumarizace. The results of the proposed system were compared with the results of sentence summarization in the reference articles. The evaluation was performed using co-occurrence of n-grams in the reference and generated summaries, and by readers mark-ups. The readers marked two aspects of the summaries: readability and information relevance. Experiments confirm that the generated summaries have the same information value as the reference summaries. However, readers state that phrase summaries are hard to read without the whole sentence context.
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Notes
- 1.
Sentences are used as segments when the text is summarized.
References
Mateju, L., Cerva, P., Zdansky, J.: Investigation into the use of deep neural networks for LVCSR of Czech. In: 2015 IEEE International Workshop of Electronics, Control, Measurement, Signals and their Application to Mechatronics (ECMSM), pp. 1–4. IEEE (2015)
Nouza, J., Zdansky, J., Cerva, P., Silovsky, J.: Challenges in speech processing of Slavic languages (case studies in speech recognition of Czech and Slovak). In: Esposito, A., Campbell, N., Vogel, C., Hussain, A., Nijholt, A. (eds.) Second COST 2102. LNCS, vol. 5967, pp. 225–241. Springer, Heidelberg (2010)
Bohac, M., Blavka, K., Kucharova, M., Skodova, S.: Post-processing of the recognized speech for web presentation of large audio archive. In: 2012 35th International Conference on Telecommunications and Signal Processing (TSP), pp. 441–445 (2012)
Kovář, V., Horák, A., Jakubíček, M.: Syntactic analysis using finite patterns: a new parsing system for Czech. In: Vetulani, Z. (ed.) LTC 2009. LNCS, vol. 6562, pp. 161–171. Springer, Heidelberg (2011)
Hori, C., Furui, S., Malkin, R., Yu, H., Waibel, A.: Automatic speech summarization applied to English broadcast news speech. In: 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), vol. 1, pp. I-9–I-12 (2002)
Chen, Y.T., Chen, B., Wang, H.M.: A probabilistic generative framework for extractive broadcast news speech summarization. IEEE Trans. Audio Speech Lang. Process. 17, 95–106 (2009)
Furui, S., Kikuchi, T., Shinnaka, Y., Hori, C.: Speech-to-text and speech-to-speech summarization of spontaneous speech. IEEE Trans. Speech Audio Process. 12, 401–408 (2004)
Straková, J., Straka, M., Hajič, J.: Open-source tools for morphology, lemmatization, POS tagging and named entity recognition. In: Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp. 13–18. Association for Computational Linguistics, Baltimore, Maryland (2014)
Rott, M., Červa, P.: SummEC: a summarization engine for Czech. In: Habernal, I. (ed.) TSD 2013. LNCS, vol. 8082, pp. 527–535. Springer, Heidelberg (2013)
Michal, R.: The initial study of term vector generation methods for news summarization. In: Proceedings of the Ninth Workshop on Recent Advances in Slavonic Natural Languages Processing, pp. 23–30. Tribun EU, Brno (2015)
Vanderwende, L., Suzuki, H., Brockett, C., Nenkova, A.: Beyond sumbasic: task-focused summarization with sentence simplification and lexical expansion. Inf. Process. Manag. 43, 1606–1618 (2007). Text Summarization
Lin, C.Y.: Rouge: a package for automatic evaluation of summaries, pp. 25–26 (2004)
Dahl, G.E., Yu, D., Deng, L., Acero, A.: Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition. IEEE Trans. Audio Speech Lang. Process. 20, 30–42 (2012)
Svendsen, T., Hamar, J.B.: Combining NDHMM and phonetic feature detection for speech recognition. In: 2015 23rd European Signal Processing Conference (EUSIPCO), pp. 1666–1670 (2015)
Acknowledgement
This paper was supported by the Technology Agency of the Czech Republic (Project No. TA04010199) and by the Student Grant Scheme 2016 (SGS) at the Technical University of Liberec.
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Rott, M., Červa, P. (2016). Speech-to-Text Summarization Using Automatic Phrase Extraction from Recognized Text. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds) Text, Speech, and Dialogue. TSD 2016. Lecture Notes in Computer Science(), vol 9924. Springer, Cham. https://doi.org/10.1007/978-3-319-45510-5_12
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