In this paper, we propose a supervised model for ranking 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 which includes our estimation of word importance results in summaries comparable with the state-of-the-art by automatic evaluation. Disciplines Computer Engineering | Computer Sciences Comments University of Pennsylvania Department of Computer and Information Science Technical Report No. MSCIS-14-02. This technical report is available at ScholarlyCommons: http://repository.upenn.edu/cis_reports/989 Improving the Estimation of Word Importance for News Multi-Document Summarization Extended Technical Report Kai Hong University of Pennsylvania Philadelphia, PA, 19104 hongkai1@seas.upenn.edu Ani Nenkova University of Pennsylvania Philadelphia, PA, 19104 nenkova@seas.upenn.edu
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Improving the Estimation of Word Importance for News Multi-Document Summarization
Semantic Scholar · Computer Science · 2014
Abstract
In this paper, we propose a supervised model for ranking 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 which includes our estimation of word importance results in summaries comparable with the state-of-the-art by automatic evaluation. Disciplines Computer Engineering | Computer Sciences Comments University of Pennsylvania Department of Computer and Information Science Technical Report No. MSCIS-14-02. This technical report is available at ScholarlyCommons: http://repository.upenn.edu/cis\_reports/989 Improving the Estimation of Word Importance for News Multi-Document Summarization Extended Technical Report Kai Hong University of Pennsylvania Philadelphia, PA, 19104 hongkai1@seas.upenn.edu Ani Nenkova University of Pennsylvania Philadelphia, PA, 19104 nenkova@seas.upenn.edu