Spoken document summarization using relevant information

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 for extractive spoken document summarization. In the proposed methods, the probability of a document being generated by a sentence is modeled by a hidden Markov model (HMM), while the retrieved relevant text documents are used to estimate the HMM's parameters and the sentence's prior probability. The results of experiments on Chinese broadcast news compiled in Taiwan show that the new methods outperform the previous HMM approach.