  We present a document compression system that uses a hierarchical
noisy-channel model of text production. Our compression system first
automatically derives the syntactic structure of each sentence and the overall
discourse structure of the text given as input. The system then uses a
statistical hierarchical model of text production in order to drop
non-important syntactic and discourse constituents so as to generate coherent,
grammatical document compressions of arbitrary length. The system outperforms
both a baseline and a sentence-based compression system that operates by
simplifying sequentially all sentences in a text. Our results support the claim
that discourse knowledge plays an important role in document summarization.

Computation and Language