  Extractive summarization aims at selecting a set of indicative sentences from
a source document as a summary that can express the major theme of the
document. A general consensus on extractive summarization is that both
relevance and coverage are critical issues to address. The existing methods
designed to model coverage can be characterized by either reducing redundancy
or increasing diversity in the summary. Maximal margin relevance (MMR) is a
widely-cited method since it takes both relevance and redundancy into account
when generating a summary for a given document. In addition to MMR, there is
only a dearth of research concentrating on reducing redundancy or increasing
diversity for the spoken document summarization task, as far as we are aware.
Motivated by these observations, two major contributions are presented in this
paper. First, in contrast to MMR, which considers coverage by reducing
redundancy, we propose two novel coverage-based methods, which directly
increase diversity. With the proposed methods, a set of representative
sentences, which not only are relevant to the given document but also cover
most of the important sub-themes of the document, can be selected
automatically. Second, we make a step forward to plug in several
document/sentence representation methods into the proposed framework to further
enhance the summarization performance. A series of empirical evaluations
demonstrate the effectiveness of our proposed methods.
