文章基本信息

标题：VSMbM: A New Metric for Automatically Generated Text Summaries Evaluation
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作者：Alaidine Ben Ayed ; Ismaïl Biskri ; Jean-Guy Meunier 等
期刊名称：Computer Science & Information Technology
电子版ISSN：2231-5403
出版年度：2020
卷号：10
期号：5
页码：121-128
DOI：10.5121/csit.2020.100510
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：In this paper, we present VSMbM; a new metric for automatically generated text summaries evaluation. VSMbM is based on vector space modelling. It gives insights on to which extent retention and fidelity are met in the generated summaries. Two variants of the proposed metric, namely PCA-VSMbM and ISOMAP VSMbM, are tested and compared to Recall-Oriented Understudy for Gisting Evaluation (ROUGE): a standard metric used to evaluate automatically generated summaries. Conducted experiments on the Timeline17 dataset show that VSMbM scores are highly correlated to the state-of-the-art Rouge scores.
关键词：Automatic Text Summarization ;Automatic summary evaluation ;Vector space modelling.