Abstract
A growing number of universities offer recordings of lectures, seminars and talks in an online e-learning portal. However, the user is often not interested in the entire recording, but is looking for parts covering a certain topic. Usually, the user has to either watch the whole video or “zap” through the lecture and risk missing important details. We present an integrated web-based platform to help users find relevant sections within recorded lecture videos by providing them with a ranked list of key phrases. For a user-defined subset of these, a StreamGraph visualizes when important key phrases occur and how prominent they are at the given time. To come up with the best key phrase rankings, we evaluate three different key phrase ranking methods using lectures of different topics by comparing automatic with human rankings, and show that human and automatic rankings yield similar scores using Normalized Discounted Cumulative Gain (NDCG).
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
Preview
Unable to display preview. Download preview PDF.
References
Burnard, L. (ed.): Reference Guide for the British National Corpus. Research Technologies Service at Oxford University Computing Services (February 2007)
Byron, L., Wattenberg, M.: Stacked Graphs – Geometry & Aesthetics. IEEE Transactions on Visualization and Computer Graphics (TVCG) 14(6), 1245–1252 (2008)
Gropp, M.: Key Phrases for the Textual and Visual Summarization of Academic Spoken Language. Studienarbeit Informatik, Dept. Informatik 5, Univ. Erlangen-Nuremberg (2010)
Järvelin, K., Kekäläinen, J.: Cumulated gain-based evaluation of ir techniques. ACM Trans. Inf. Syst. 20(4), 422–446 (2002)
Jiang, X., Hu, Y., Li, H.: A ranking approach to keyphrase extraction. In: Proc. ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 756–757 (2009)
Liu, F., Liu, F., Liu, Y.: Automatic keyword extraction for the meeting corpus using supervised approach and bigram expansion. In: Proc. IEEE Workshop on Spoken Language Technologies (SLT), pp. 181–184 (2008)
Liu, F., Pennell, D., Liu, F., Liu, Y.: Unsupervised approaches for automatic keyword extraction using meeting transcripts. In: Proc. NAACL-HLT, pp. 620–628. ACL, Stroudsburg (2009)
Liu, T.Y.: Learning to Rank for Information Retrieval. Foundations and Trends in Information Retrieval 3 (2009)
Phan, X.: CRFTagger: CRF English POS Tagger (2006), http://crftagger.sourceforge.net
Porter, M.: An algorithm for suffix stripping. Program 14(3), 130–137 (1980)
Riedhammer, K., Favre, B., Hakkani-Tür, D.: A Keyphrase Based Approach to Interactive Meeting Summarization. In: Proc. IEEE Workshop on Spoken Language Technologies (SLT), pp. 153–156 (2008)
Riedhammer, K., Favre, B., Hakkani-Tür, D.: Long Story Short – Global Unsupervised Models for Keyphrase Based Meeting Summarization. Speech Communication 52(10), 801–815 (2010)
Tomokiyo, T., Hurst, M.: A Language Model Approach to Keyphrase Extraction. In: Proc. ACL Workshop on Multiword Expressions, pp. 33–40 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gropp, M., Nöth, E., Riedhammer, K. (2011). A Novel Lecture Browsing System Using Ranked Key Phrases and StreamGraphs. In: Habernal, I., Matoušek, V. (eds) Text, Speech and Dialogue. TSD 2011. Lecture Notes in Computer Science(), vol 6836. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23538-2_3
Download citation
DOI: https://doi.org/10.1007/978-3-642-23538-2_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23537-5
Online ISBN: 978-3-642-23538-2
eBook Packages: Computer ScienceComputer Science (R0)