论文标题
使用合成事实评估自动文本摘要
Evaluation of Automatic Text Summarization using Synthetic Facts
论文作者
论文摘要
尽管最近有了一些进步，但自动文本摘要仍然不可靠，难以捉摸，并且在应用程序中的实际用途有限。当前汇总方法的两个主要问题是众所周知的：评估和事实一致性。为了解决这些问题，我们提出了一个新的无参考文本摘要评估系统，该系统可以根据事实一致性，全面性和压缩率来衡量任何文本摘要模型的质量。据我们所知，我们的评估系统是第一个基于事实，信息覆盖率和压缩率的文本摘要模型的总体质量的系统。
Despite some recent advances, automatic text summarization remains unreliable, elusive, and of limited practical use in applications. Two main problems with current summarization methods are well known: evaluation and factual consistency. To address these issues, we propose a new automatic reference-less text summarization evaluation system that can measure the quality of any text summarization model with a set of generated facts based on factual consistency, comprehensiveness, and compression rate. As far as we know, our evaluation system is the first system that measures the overarching quality of the text summarization models based on factuality, information coverage, and compression rate.
