Advertisement

Extractive Text-Based Summarization of Arabic Videos: Issues, Approaches and Evaluations

  • Mohamed Amine MenacerEmail author
  • Carlos-Emiliano González-Gallardo
  • Karima Abidi
  • Dominique Fohr
  • Denis Jouvet
  • David Langlois
  • Odile Mella
  • Fatiha Sadat
  • Juan-Manuel Torres-Moreno
  • Kamel Smaïli
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1108)

Abstract

In this paper, we present and evaluate a method for extractive text-based summarization of Arabic videos. The algorithm is proposed in the scope of the AMIS project that aims at helping a user to understand videos given in a foreign language (Arabic). For that, the project proposes several strategies to translate and summarize the videos. One of them consists in transcribing the Arabic videos, summarizing the transcriptions, and translating the summary. In this paper we describe the video corpus that was collected from YouTube and present and evaluate the transcription-summarization part of this strategy. Moreover, we present the Automatic Speech Recognition (ASR) system used to transcribe the videos, and show how we adapted this system to the Algerian dialect. Then, we describe how we automatically segment into sentences the sequence of words provided by the ASR system, and how we summarize the obtained sequence of sentences. We evaluate objectively and subjectively our approach. Results show that the ASR system performs well in terms of Word Error Rate on MSA, but needs to be adapted for dealing with Algerian dialect data. The subjective evaluation shows the same behaviour than ASR: transcriptions for videos containing dialectal data were better scored than videos containing only MSA data. However, summaries based on transcriptions are not as well rated, even when transcriptions are better rated. Last, the study shows that features, such as the lengths of transcriptions and summaries, and the subjective score of transcriptions, explain only 31% of the subjective score of summaries.

Keywords

Text summarization Video summarization Automatic speech recognition Segmentation 

Notes

Acknowledgment

We acknowledge the support of Chist-Era for funding this research through the AMIS (Access Multilingual Information opinionS) project.

References

  1. 1.
    Abidi, K., Menacer, M.A., Smaili, K.: CALYOU: a comparable spoken algerian corpus harvested from Youtube. In: 18th Annual Conference of the International Communication Association (Interspeech) (2017)Google Scholar
  2. 2.
    Ali, A., Zhang, Y., Cardinal, P., Dahak, N., Vogel, S., Glass, J.: A complete Kaldi recipe for building Arabic speech recognition systems. In: 2014 IEEE Spoken Language Technology Workshop (SLT), December 2014, pp. 525–529 (2014).  https://doi.org/10.1109/SLT.2014.7078629
  3. 3.
    Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching word vectors with subword information. Trans. Assoc. Comput. Linguist. 5, 135–146 (2017)CrossRefGoogle Scholar
  4. 4.
    Che, X., Wang, C., Yang, H., Meinel, C.: Punctuation prediction for unsegmented transcript based on word vector. In: LREC (2016)Google Scholar
  5. 5.
    Galliano, S., Geoffrois, E., Mostefa, D., Choukri, K., Bonastre, J.F., Gravier, G.: The ESTER phase II evaluation campaign for the rich transcription of French broadcast news. In: Ninth European Conference on Speech Communication and Technology (2005)Google Scholar
  6. 6.
    González-Gallardo, C.E., Pontes, E.L., Sadat, F., Torres-Moreno, J.M.: Automated sentence boundary detection in modern standard Arabic transcripts using deep neural networks. Procedia Comput. Sci. 142, 339–346 (2018)CrossRefGoogle Scholar
  7. 7.
    Gotoh, Y., Renals, S.: Sentence boundary detection in broadcast speech transcripts. In: ASR 2000-Automatic Speech Recognition: Challenges for the New Millenium ISCA Tutorial and Research Workshop (ITRW) (2000)Google Scholar
  8. 8.
    Harrat, S., Meftouh, K., Abbas, M., Smaïli, K.: Grapheme to phoneme conversion - an Arabic dialect case. In: Spoken Language Technologies for Under-resourced Languages (2014)Google Scholar
  9. 9.
    Harrat, S., Meftouh, K., Smaïli, K.: Creating parallel Arabic dialect corpus: pitfalls to avoid. In: 18th International Conference on Computational Linguistics and Intelligent Text Processing (CICLING), Budapest, Hungary, April 2017. https://hal.archives-ouvertes.fr/hal-01557405
  10. 10.
    Harrat, S., Meftouh, K., Smaïli, K.: Maghrebi Arabic dialect processing: an overview. J. Int. Sci. Gen. Appl. 1 (2018). https://hal.archives-ouvertes.fr/hal-01873779
  11. 11.
    Leszczuk, M., Grega, M., Koźbiał, A., Gliwski, J., Wasieczko, K., Smaïli, K.: Video summarization framework for newscasts and reports – work in progress. In: Dziech, A., Czyżewski, A. (eds.) MCSS 2017. CCIS, vol. 785, pp. 86–97. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-69911-0_7CrossRefGoogle Scholar
  12. 12.
    Linhares Pontes, E., González-Gallardo, C.-E., Torres-Moreno, J.-M., Huet, S.: Cross-lingual speech-to-text summarization. In: Choroś, K., Kopel, M., Kukla, E., Siemiński, A. (eds.) MISSI 2018. AISC, vol. 833, pp. 385–395. Springer, Cham (2019).  https://doi.org/10.1007/978-3-319-98678-4_39CrossRefGoogle Scholar
  13. 13.
    Makhoul, J., et al.: The effects of speech recognition and punctuation on information extraction performance. In: Ninth European Conference on Speech Communication and Technology (2005)Google Scholar
  14. 14.
    Meftouh, K., Harrat, S., Smaïli, K.: PADIC: extension and new experiments. In: 7th International Conference on Advanced Technologies ICAT. Antalya, Turkey, April 2018. https://hal.archives-ouvertes.fr/hal-01718858
  15. 15.
    Meftouh, K., Bouchemal, N., Smaïli, K.: A study of a non-resourced language: the case of one of the algerian dialects. In: The Third International Workshop on Spoken Languages Technologies for Under-Resourced Languages - SLTU 2012, Cape-town, South Africa, May 2012, pp. 1–7 (2012). https://hal.archives-ouvertes.fr/hal-00727042
  16. 16.
    Meftouh, K., Harrat, S., Jamoussi, S., Abbas, M., Smaili, K.: Machine translation experiments on PADIC: a parallel Arabic dialect corpus. In: Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation, pp. 26–34 (2015)Google Scholar
  17. 17.
    Menacer, M.A., Mella, O., Fohr, D., Jouvet, D., Langlois, D., Smaïli, K.: Development of the Arabic Loria Automatic Speech Recognition system (ALASR) and its evaluation for Algerian dialect. In: ACLing 2017–3rd International Conference on Arabic Computational Linguistics, Dubai, United Arab Emirates, November 2017, pp. 1–8 (2017). https://hal.archives-ouvertes.fr/hal-01583842CrossRefGoogle Scholar
  18. 18.
    Mrozinski, J., Whittaker, E.W., Chatain, P., Furui, S.: Automatic sentence segmentation of speech for automatic summarization. In: 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings, vol. 1, p. I. IEEE (2006)Google Scholar
  19. 19.
    Porter, M.F.: An algorithm for suffix stripping. Program 14(3), 130–137 (1980)CrossRefGoogle Scholar
  20. 20.
    Povey, D., et al.: The Kaldi speech recognition toolkit. In: IEEE 2011 Workshop on Automatic Speech Recognition and Understanding. In: IEEE Signal Processing Society (2011). iEEE Catalog No.: CFP11SRW-USBGoogle Scholar
  21. 21.
    Shriberg, E., Stolcke, A., Hakkani-Tür, D., Tür, G.: Prosody-based automatic segmentation of speech into sentences and topics. Speech Commun. 32(1–2), 127–154 (2000)CrossRefGoogle Scholar
  22. 22.
    Stolcke, A.: Entropy-based pruning of backoff language models. arXiv preprint cs/0006025 (2000)Google Scholar
  23. 23.
    Strassel, S.: Simple metadata annotation specification V5, January 2003. http://www.ldc.upenn.edu/Projects/MDE/Guidelines/SimpleMDE
  24. 24.
    Torres-Moreno, J.M.: Artex is anotheR TEXt summarizer. arXiv preprint arXiv:1210.3312 (2012)
  25. 25.
    Torres-Moreno, J.M.: Automatic Text Summarization. Wiley, London (2014)CrossRefGoogle Scholar
  26. 26.
    Yu, D., Deng, L.: Automatic Speech Recognition. Springer, London (2016).  https://doi.org/10.1007/978-1-4471-5779-3CrossRefzbMATHGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Mohamed Amine Menacer
    • 1
    Email author
  • Carlos-Emiliano González-Gallardo
    • 2
  • Karima Abidi
    • 1
  • Dominique Fohr
    • 1
  • Denis Jouvet
    • 1
  • David Langlois
    • 1
  • Odile Mella
    • 1
  • Fatiha Sadat
    • 3
  • Juan-Manuel Torres-Moreno
    • 2
    • 4
  • Kamel Smaïli
    • 1
  1. 1.Loria, University of LorraineNancyFrance
  2. 2.LIA, Avignon UniversitéAvignonFrance
  3. 3.UQAMMontrealCanada
  4. 4.Poliyechnique MontréalMontrealCanada

Personalised recommendations