Keywords, that is, content-relevant words in summaries play an important role in efficient information conveyance, making it critical to assess if system-generated summaries contain such informative words during evaluation. However, existing evaluation metrics for extreme summarization models do not pay explicit attention to keywords in summaries, leaving developers ignorant of their presence. To address this issue, we present a keyword-oriented evaluation metric, dubbed ROUGE-K, which provides a quantitative answer to the question of -- \textit{How well do summaries include keywords?} Through the lens of this keyword-aware metric, we surprisingly find that a current strong baseline model often misses essential information in their summaries. Our analysis reveals that human annotators indeed find the summaries with more keywords to be more relevant to the source documents. This is an important yet previously overlooked aspect in evaluating summarization systems. Finally, to enhance keyword inclusion, we propose four approaches for incorporating word importance into a transformer-based model and experimentally show that it enables guiding models to include more keywords while keeping the overall quality. Our code is released at https://github.com/sobamchan/rougek.

翻译：关键词，即摘要中与内容相关的词汇，在高效信息传达中起着重要作用，这使得在评估过程中判断系统生成的摘要是否包含此类信息性词汇至关重要。然而，现有面向极端摘要模型的评估指标并未明确关注摘要中的关键词，导致开发者对其存在性一无所知。为解决这一问题，我们提出了一种面向关键词的评估指标——ROUGE-K，它能够量化回答"摘要包含关键词的程度如何？"这一问题。通过这一关键词感知指标的视角，我们惊讶地发现，当前强基线模型生成的摘要常常遗漏关键信息。我们的分析表明，人工标注员确实认为包含更多关键词的摘要与源文档的相关性更高。这是评估摘要系统时一个此前被忽视的重要方面。最后，为增强关键词包含性，我们提出了四种将词汇重要性融入基于Transformer模型的方法，并通过实验证明，该方法能在保持整体质量的同时引导模型包含更多关键词。我们的代码已发布于https://github.com/sobamchan/rougek。