Practical summarization systems are expected to produce summaries of varying lengths, per user needs. While a couple of early summarization benchmarks tested systems across multiple summary lengths, this practice was mostly abandoned due to the assumed cost of producing reference summaries of multiple lengths. In this paper, we raise the research question of whether reference summaries of a single length can be used to reliably evaluate system summaries of multiple lengths. For that, we have analyzed a couple of datasets as a case study, using several variants of the ROUGE metric that are standard in summarization evaluation. Our findings indicate that the evaluation protocol in question is indeed competitive. This result paves the way to practically evaluating varying-length summaries with simple, possibly existing, summarization benchmarks.

We present a novel interactive summarization system that is based on abstractive summarization, derived from a recent consolidated knowledge representation for multiple texts. We incorporate a couple of interaction mechanisms, providing a bullet-style summary while allowing to attain the most important information first and interactively drill down to more specific details. A usability study of our implementation, for event news tweets, suggests the utility of our approach for text exploration.