The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
The Sharable Content Object Reference Model (SCORM)—A Critical Review
ICCE '02 Proceedings of the International Conference on Computers in Education
Speech-based and video-supported indexing of multimedia broadcast news
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
SAINT-W '05 Proceedings of the 2005 Symposium on Applications and the Internet Workshops
Automatic metadata extraction and indexing for reusing e-learning multimedia objects
Workshop on multimedia information retrieval on The many faces of multimedia semantics
Towards to an automatic semantic annotation for multimedia learning objects
Proceedings of the international workshop on Educational multimedia and multimedia education
ALOA: A Web Services Driven Framework for Automatic Learning Object Annotation
EC-TEL '08 Proceedings of the 3rd European conference on Technology Enhanced Learning: Times of Convergence: Technologies Across Learning Contexts
The automatic creation of literature abstracts
IBM Journal of Research and Development
Opinosis: a graph-based approach to abstractive summarization of highly redundant opinions
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Integrating Navigational and Structural Information in SCORM Content Aggregation Modeling
ICALT '12 Proceedings of the 2012 IEEE 12th International Conference on Advanced Learning Technologies
Automatic generation of SCORM compliant metadata for portable document format files
Proceedings of the 13th International Conference on Computer Systems and Technologies
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Automatic Metadata Generation in the context of e-learning standards is usually referred to algorithms able to process and annotate semi structured documents in plain text. As most of the information available on the web nowadays is unstructured and in the form of multimedia files, the need for more general approaches arises. We propose an automatic metadata generation procedure that allows to label specific unstructured data (video lectures) with metadata compliant to the Learning Object Metadata standard. After preprocessing, three different summarization algorithms are tested and used to obtain a synthetic description of video content, both in terms of Description and Title. Results show that, in the provided context, the obtained Description has a good agreement with the lesson abstract written by its author.