Files for Peer reveiw (Sentence Compression Using Constituency Analysis of Sentence Structure)

Simply stated, producing a shorter format of a given sentence is the task of sentence compression. The challenging part of this process is preserving the most important information as well as grammaticality in the compressed version. There are different ways of achieving this purpose, among which, we try to come up with a rule-based extractive method for the Persian language. Our approach involves identifying removable constituents within a sentence and eliminating them in the order of insignificance until the desired compression rate is achieved. To develop this method, we created a compression corpus of 600 sentences from two available treebanks in the Persian language. 300 sentences are used for extracting deletion rules as training data and the remaining 300 are used for testing the system. Its applicability even using a limited training corpus and user’s authority over the input compression rate are the benefits of the presented method in this paper. Additionally, our compression system is open-ended, enabling the addition or removal of deletion rules to refine its performance. The results suggest applying rule-based methods for language processing tasks can be quite efficient in syntactically rich languages such as Persian, yielding desirable outcomes and offering distinct advantages.