A comparative study of probabilistic ranking models for spoken document summarization

The purpose of extractive document 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 present a comparative study of various supervised and unsupervised probabilistic ranking models for spoken document summarization on the Chinese broadcast news. Moreover, we also investigate the possibility of using unsupervised summarizers to boost the performance of supervised summarizers when manual labels are not available for the training of supervised summarizers. Encouraging results were initially demonstrated.