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.
REF introduced a supervised model for predicting word importance that incorporated a rich set of features.
Improving the Estimation of Word Importance for News Multi-Document Summarization
EACL
Computer Science