Exploiting Semantic Term Relations in Text Summarization

摘要

The traditional frequency-based approach to creating multi-document extractive summary ranks sentences based on scores computed by summing up TF*IDF weights of words contained in the sentences. In this approach, TF or term frequency is calculated based on how frequently a term (word) occurs in the input, and TF calculated in this way does not take into account the semantic relations among terms. In this paper, the authors propose methods that exploit semantic term relations for improving sentence ranking and redundancy removal steps of a summarization system. The proposed summarization system has been tested on DUC 2003 and DUC 2004 benchmark multi-document summarization datasets. The experimental results reveal that performance of the multi-document text summarizer is significantly improved when the distributional term similarity measure is used for finding semantic term relations. The multi-document text summarizer also outperforms some well-known summarization baselines to which it is compared.