Improving the Estimation of Word Importance for News Multi-Document Summarization
Kai Sze Hong, Ani Nenkova · 2014
We introduce a supervised model for predicting word importance that incorporates a rich set of features.Our model is superior to prior approaches for identifying words used in human summaries.Moreover we show that an extractive summarizer using these estimates of word importance is comparable in automatic evaluation with the state-of-the-art.