Spoken lecture summarization by random walk over a graph constructed with automatically extracted key terms
Yun-Nung Chen, Yu Huang, Ching-Feng Yeh, Lin-shan Lee · 2011
This paper proposes an improved approach for spoken lecture summarization, in which random walk is performed on a graph constructed with automatically extracted key terms and proba-bilistic latent semantic analysis (PLSA). Each sentence of the document is represented as a node of the graph and the edge be-tween two nodes is weighted by the topical similarity between the two sentences. The basic idea is that sentences topically similar to more important sentences should be more important. In this way all sentences in the document can be jointly consid-ered more globally rather than individually. Experimental re-sults showed significant improvement in terms of ROUGE eval-uation. Index Terms: summarization, course lecture, probabilistic la-tent semantic analysis (PLSA), random walk, key term