Spoken Lecture Summarization by Random Walk over a Graph Constructed with Automatically Extracted Key Terms

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 probabilistic latent semantic analysis (PLSA). Each sentence of the document is represented as a node of the graph and the edge between 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 considered more globally rather than individually. Experimental results showed significant improvement in terms of ROUGE evaluation.