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.
Using a noisy-channel model, REF exploit the discourse structure of a document and the syntactic structure of its sentences in order to decide which constituents to drop but also which discourse units are unimportant.
A Noisy-Channel Model For Document Compression
Annual Meeting Of The Association For Computational Linguistics
Computer Science