| Paper: | SLP-P15.8 | ||
| Session: | Spoken Document Search, Navigation and Summarization | ||
| Time: | Thursday, May 18, 14:00 - 16:00 | ||
| Presentation: | Poster | ||
| Topic: | Speech and Spoken Language Processing: Speech data mining and document retrieval | ||
| Title: | CHINESE SPOKEN DOCUMENT SUMMARIZATION USING PROBABILISTIC LATENT TOPICAL INFORMATION | ||
| Authors: | Berlin Chen, Yao-Ming Yeh, Yao-Min Huang, Yi-Ting Chen, National Taiwan Normal University, Taiwan | ||
| Abstract: | 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 the paper, we proposed the use of probabilistic latent topical information for extractive summarization of spoken documents. Various kinds of modeling structures and learning approaches were extensively investigated. In addition, the summarization capabilities were verified by comparison with the conventional vector space model and latent semantic indexing model, as well as the HMM model. The experiments were performed on the Chinese broadcast news collected in Taiwan. Noticeable performance gains were obtained. | ||
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