Extractive Spoken Document Summarization with Representation Learning Techniques

The rapidly increasing availability of multimedia associated with spoken documents on the Internet has prompted automatic spoken document summarization to be an important research subject. Thus far, the majority of existing work has focused on extractive spoken document summarization, which selects salient sentences from an original spoken document according to a target summarization ratio and concatenates them to form a summary concisely, in order to convey the most important theme of the document. On the other hand, there has been a surge of interest in developing representation learning techniques for a wide variety of natural language processing (NLP)-related tasks. However, to our knowledge, they are largely unexplored in the context of extractive spoken document summarization. With the above background, this study explores a novel use of both word and sentence representation techniques for extractive spoken document summarization. In addition, three variants of sentence ranking models building on top of such representation techniques are proposed. Furthermore, extra information cues like the prosodic features extracted from spoken documents, apart from the lexical features, are also employed for boosting the summarization performance. A series of experiments conducted on the MATBN broadcast news corpus indeed reveal the performance merits of our proposed summarization methods in relation to several state-of-the-art baselines.