HUNUS at TAC 2009: with Better Performance in Update Summarization Task
نویسندگان
چکیده
In this paper, a new semantic-based extractive summary methodology is put forward. The approach makes use of WordNet synset to obtain sentence semantic similarity. The scoring function is expressed as a linear combination of two features: the query-independent feature and query-related feature. An efficient scoring function of considering historical information for the update summarization task is proposed in this paper, which considering historical information in the sentence scoring stage instead of considering them after the sentences are already scored. The system architecture as well as its linguistics processing parts are described. Finally, we present the results of our participation in TAC 2009 with possible perspectives.
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