  Due to its promise to alleviate information overload, text summarization has
attracted the attention of many researchers. However, it has remained a serious
challenge. Here, we first prove empirical limits on the recall (and F1-scores)
of extractive summarizers on the DUC datasets under ROUGE evaluation for both
the single-document and multi-document summarization tasks. Next we define the
concept of compressibility of a document and present a new model of
summarization, which generalizes existing models in the literature and
integrates several dimensions of the summarization, viz., abstractive versus
extractive, single versus multi-document, and syntactic versus semantic.
Finally, we examine some new and existing single-document summarization
algorithms in a single framework and compare with state of the art summarizers
on DUC data.
