Generate one-sentence summary of the following document.
Keywords in extremely short 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 this end, we present a keyword-oriented evaluation metric, dubbed ROUGE-K, which provides a quantitative answer to a question of -- How well do summaries include keywords? A series of analyses, including manual evaluation, shows that it well captures an important yet previously overlooked aspect in summaries. Through the lens of this keyword-aware metric, we surprisingly find that a current strong baseline model often misses essential information in their summaries. Finally, to enhance keyword inclusion, we propose four lightweight 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.