The spoken documents are very difficult to be shown on the screen, and very difficult to retrieve and browse. It is therefore important to develop technologies to summarize the entire archives of the huge quantities of spoken documents in the network content to help the user in browsing and retrieval. In this paper we propose a complete set of multi-layered technologies to handle at least some of the above issues: (1) Automatic Generation of Titles and Summaries for each of the spoken documents, such that the spoken documents become much more easier to browse, (2) Global Semantic Structuring of the entire spoken document archive, offering to the user a global picture of the semantic structure of the archive, and (3) Query-based Local Semantic Structuring for the subset of the spoken documents retrieved by the user’s query, providing the user the detailed semantic structure of the relevant spoken documents given the query he entered. The Probabilistic Latent Semantic Analysis (PLSA) is found to be helpful, and an initial prototype system for the functions mentioned above has been successfully developed, in which the broadcast news archive in Mandarin Chinese is taken as the example archive.