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Temporal Lecture Video Fragmentation Using Word Embeddings

  • Damianos Galanopoulos
  • Vasileios MezarisEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11296)

Abstract

In this work the problem of temporal video lecture fragmentation in meaningful parts is addressed. The visual content of lecture video can not be effectively used for this task due to its extremely homogeneous content. A new method for lecture video fragmentation in which only automatically generated speech transcripts of a video are exploited, is proposed. Contrary to previously proposed works that employ visual, audio and textual features and use time-consuming supervised methods which require annotated training data, we present a method that analyses the transcripts’ text with the help of word embeddings that are generated from pre-trained state-of-the-art neural networks. Furthermore, we address a major problem of video lecture fragmentation research, which is the lack of large-scale datasets for evaluation, by presenting a new artificially-generated dataset of synthetic video lecture transcripts that we make publicly available. Experimental comparisons document the merit of the proposed approach.

Keywords

Lecture video fragmentation Word embeddings Video segmentation 

Notes

Acknowledgements

This work was supported by the EUs Horizon 2020 research and innovation programme under grant agreement No. 693092 MOVING. We are grateful to JSI/VideoLectures.NET for providing the lectures transcripts.

References

  1. 1.
    Basu, S., Yu, Y., Singh, V.K., Zimmermann, R.: Videopedia: lecture video recommendation for educational blogs using topic modeling. In: Tian, Q., Sebe, N., Qi, G.-J., Huet, B., Hong, R., Liu, X. (eds.) MMM 2016. LNCS, vol. 9516, pp. 238–250. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-27671-7_20CrossRefGoogle Scholar
  2. 2.
    Bhatt, C.A., et al.: Multi-factor segmentation for topic visualization and recommendation: the MUST-VIS system. In: Proceedings of the 21st ACM International Conference on Multimedia, pp. 365–368. ACM (2013)Google Scholar
  3. 3.
    Brants, T., Chen, F., Tsochantaridis, I.: Topic-based document segmentation with probabilistic latent semantic analysis. In: Proceedings of the 11th International Conference on Information and Knowledge Management, CIKM 2002, pp. 211–218. ACM, New York (2002)Google Scholar
  4. 4.
    Che, X., Yang, H., Meinel, C.: Lecture video segmentation by automatically analyzing the synchronized slides. In: Proceedings of the 21st ACM International Conference on Multimedia, pp. 345–348. ACM (2013)Google Scholar
  5. 5.
    Chen, H., Cooper, M., Joshi, D., Girod, B.: Multi-modal language models for lecture video retrieval. In: Proceedings of the 22nd ACM International Conference on Multimedia, pp. 1081–1084. ACM (2014)Google Scholar
  6. 6.
    Finkel, J.R., Grenager, T., Manning, C.: Incorporating non-local information into information extraction systems by Gibbs sampling. In: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, ACL 2005, pp. 363–370 (2005)Google Scholar
  7. 7.
    Glavaš, G., Nanni, F., Ponzetto, S.P.: Unsupervised text segmentation using semantic relatedness graphs. In: Association for Computational Linguistics (2016)Google Scholar
  8. 8.
    Hearst, M.A.: TextTiling: segmenting text into multi-paragraph subtopic passages. Comput. Linguist. 23(1), 33–64 (1997)Google Scholar
  9. 9.
    Koshorek, O., Cohen, A., Mor, N., Rotman, M., Berant, J.: Text segmentation as a supervised learning task. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 2 (Short Papers), pp. 469–473 (2018)Google Scholar
  10. 10.
    Lin, M., Chau, M., Cao, J., Nunamaker Jr., J.F.: Automated video segmentation for lecture videos: a linguistics-based approach. Int. J. Technol. Hum. Interact. (IJTHI) 1(2), 27–45 (2005)CrossRefGoogle Scholar
  11. 11.
    Ma, D., Zhang, X., Ouyang, X., Agam, G.: Lecture video indexing using boosted margin maximizing neural networks. In: 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 221–227. IEEE (2017)Google Scholar
  12. 12.
    Markatopoulou, F., Galanopoulos, D., Mezaris, V., Patras, I.: Query and keyframe representations for ad-hoc video search. In: Proceedings of the 2017 ACM on International Conference on Multimedia Retrieval, ICMR 2017, pp. 407–411. ACM (2017)Google Scholar
  13. 13.
    Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems 26, pp. 3111–3119. Curran Associates, Inc. (2013)Google Scholar
  14. 14.
    Shah, R.R., Yu, Y., Shaikh, A.D., Zimmermann, R.: TRACE: linguistic-based approach for automatic lecture video segmentation leveraging Wikipedia texts. In: 2015 IEEE International Symposium on Multimedia (ISM), pp. 217–220, December 2015Google Scholar
  15. 15.
    Shah, R.R., Yu, Y., Shaikh, A.D., Tang, S., Zimmermann, R.: ATLAS: automatic temporal segmentation and annotation of lecture videos based on modelling transition time. In: Proceedings of the 22nd ACM International Conference on Multimedia, pp. 209–212 (2014)Google Scholar
  16. 16.
    Toutanova, K., Klein, D., Manning, C.D., Singer, Y.: Feature-rich part-of-speech tagging with a cyclic dependency network. In: Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technologies, NAACL 2003, vol. 1, pp. 173–180 (2003)Google Scholar
  17. 17.
    Yang, H., Siebert, M., Luhne, P., Sack, H., Meinel, C.: Automatic lecture video indexing using video OCR technology. In: 2011 IEEE International Symposium on Multimedia, pp. 111–116, December 2011Google Scholar
  18. 18.
    Yang, H., Meinel, C.: Content based lecture video retrieval using speech and video text information. IEEE Trans. Learn. Technol. 7(2), 142–154 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Information Technologies Institute/CERTHThermi-ThessalonikiGreece

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