Enhanced language modeling for extractive speech summarization with sentence relatedness information

Extractive summarization is intended to automatically select a set of representative sentences from a text or spoken document that can concisely express the most important topics of the document. Language modeling (LM) has been proven to be a promising framework for performing extractive summarization in an unsupervised manner. However, there remain two fundamental challenges facing existing LM-based methods. One is how to construct sentence models involved in the LM framework more accurately without resorting to external information sources. The other is how to additionally take into account the sentence-level structural relationships embedded in a document for important sentence selection. To address these two challenges, in this paper we explore a novel approach that generates overlapped clusters to extract sentence relatedness information from the document to be summarized, which can be used not only to enhance the estimation of various sentence models but also to allow for the sentencelevel structural relationships for better summarization performance. Further, the utilities of our proposed methods and several state-of-the-art unsupervised methods are analyzed and compared extensively. A series of experiments conducted on a Mandarin broadcast news summarization task demonstrate the effectiveness and viability of our method.