Paper: Improving the Estimation of Word Importance for News Multi-Document Summarization

ACL ID E14-1075
Title Improving the Estimation of Word Importance for News Multi-Document Summarization
Venue Annual Meeting of The European Chapter of The Association of Computational Linguistics
Session Main Conference
Year 2014
Authors

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.