document: Summarization beyond sentence extraction : A probabilistic approach to sentence compression When humans produce summaries of documents , they do not simply extract sentences and concatenate them . Rather , they create new sentences that are grammatical , that cohere with one another , and that capture the most salient pieces of information in the original document . Given that large collections of text/abstract pairs are available online , it is now possible to envision algorithms that are trained to mimic this process . In this paper , we focus on sentence compression , a simpler version of this larger challenge . We aim to achieve two goals simultaneously : our compressions should be grammatical , and they should retain the most important pieces of information . These two goals can conflict . We devise both a noisy-channel and a decision-tree approach to the problem , and we evaluate results against manual compressions and a simple baseline
doc_bio_tags: O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O
extractive_keyphrases: sentence compression grammatical
abstractive_keyphrases: noisy-channel decision-tree document summarization