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Extractive speech summarization is intended to produce a condensed version of the original spoken document by selecting a few salient sentences from the document and concatenate them together to form a summary. In this paper, we study a novel use of manifold learning techniques for extractive speech summarization. Manifold learning has experienced a surge of research interest in various domains concerned...
The purpose of extractive summarization is to automatically select a number of indicative sentences, passages, or paragraphs from the original document according to a target summarization ratio and then sequence them to form a concise summary. In this paper, we proposed the use of probabilistic latent topical information for extractive summarization of spoken documents. Various kinds of modeling structures...
Extractive summarization usually automatically selects indicative sentences from a document according to a certain target summarization ratio, and then sequences them to form a summary. In this paper, we investigate the use of information from relevant documents retrieved from a contemporary text collection for each sentence of a spoken document to be summarized in a probabilistic generative framework...
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