Better Metrics to Automatically Predict the Quality of a Text Summary

Algorithms

Peter A. Rankel¹  John M. Conroy² 

[1] Statistics Program, Department of Mathematics, University of Maryland, College Park, MD 20742, USACenter for Computing Sciences, Institute for Defense Analyses, 17100 Science Drive, Bowie, MD 20715, USA;

关键词: multi-document summarization;    update summarization;    evaluation;    computational linguistics;    text processing;   

DOI  :  10.3390/a5040398

来源: mdpi

【 摘 要 】

In this paper we demonstrate a family of metrics for estimating the quality of a text summary relative to one or more human-generated summaries. The improved metrics are based on features automatically computed from the summaries to measure content and linguistic quality. The features are combined using one of three methods—robust regression, non-negative least squares, or canonical correlation, an eigenvalue method. The new metrics significantly outperform the previous standard for automatic text summarization evaluation, ROUGE.

【 授权许可】

CC BY   
© 2012 by the authors; licensee MDPI, Basel, Switzerland.