| sentence reduction for automatic text summarization we present a novel sentence reduction system for automatically removing extraneous phrases from sentences that are extracted from a document for summarization purpose. the system uses multiple sources of knowledge to decide which phrases in an extracted sentence can be removed, including syntactic knowledge, context information, and statistics computed from a corpus which consists of examples written by human professionals. reduction can significantly improve the conciseness of automatic summaries. we study a new method to remove extraneous phrase from sentences by using multiple source of knowledge to decide which phrase in the sentences can be removed. in our approach, decisions about which material to include/delete in the sentence summaries do not rely on relative frequency information on words, but rather on probability models of subtree deletions that are learned from a corpus of parses for sentences and their summaries. |