Salience-Based Content Characterisation Of Text Documents

Summarisation is poised to become a generally accepted solution to the larger problem of content analysis. We offer an alternative perspective on this problem, by tackling the complementary task of content characterisation; our motivation for doing so is to avoid some of the fundamental shortcomings of summarisation technologies today. Traditionally, the document summarisation task has been tackled either as a natural language processing problem, with an instantiated meaning template being rendered into coherent prose, or as a passage extraction problem, where certain fragments (typically sentences) of the source document are deemed to be highly representative of its content, and thus delivered as meaningful “approximations” of it. Balancing the conflicting requirements of depth and accuracy of a summary, on the one hand, and document and domain independence, on the other, has proven a very hard problem. This paper describes a novel approach to content characterisation of text documents. It is domainand genre-independent, by virtue of not requiring an in-depth analysis of the full meaning. At the same time, it remains closer to the core meaning by choosing a different granularity of its representations (phrasal expressions rather than sentences or paragraphs), by exploiting a notion of discourse contiguity and coherence for the purposes of uniform coverage and context maintenance, and by utilising a strong linguistic notion of salience, as a more appropriate and representative measure of a document’s “aboutness”.