Probabilistic latent semantic indexing
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Video search reranking through random walk over document-level context graph
Proceedings of the 15th international conference on Multimedia
LexRank: graph-based lexical centrality as salience in text summarization
Journal of Artificial Intelligence Research
IEEE Transactions on Audio, Speech, and Language Processing
The elements of automatic summarization
The elements of automatic summarization
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This paper proposes an improved approach to extractive summarization of spoken multi-party interaction, in which integrated random walk is performed on a graph constructed on topical/lexical relations. Each utterance is represented as a node of the graph, and the edges' weights are computed from the topical similarity between the utterances, evaluated using probabilistic latent semantic analysis (PLSA), and from word overlap. We model intra-speaker topics by partially sharing the topics from the same speaker in the graph. In this paper, we perform experiments on automatically and manually generated transcripts. For automatic transcripts, our results show that intra-speaker topic sharing and integrating topical/lexical relations can help include the important utterances.