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Knowledge-poor Multilingual Sentence Compression

We present a feature-based method for sentence compression. Firstly, a summary is created by our summarization method based on latent semantic analysis. The compression approach then removes unimportant clauses from the summary sentences. For each sentence a set of its possible compressed forms (compression candidates) is created. The candidates are then classified using 8 proposed features into two classes: in the first class there are candidates in which the important information was removed by compression and in the second class the information was still contained. The shortest candidate from the latter group substitutes the full sentence in the summary. The features are knowledge-poor which enables them to work with whatever language and the method can be easily extended by other features.