With the most advanced natural language processing and artificial
intelligence approaches, effective summarization of long and multi-topic
documents -- such as academic papers -- for readers from different domains
still remains a challenge. To address this, we introduce ConceptEVA, a
mixed-initiative approach to generate, evaluate, and customize summaries for
long and multi-topic documents. ConceptEVA incorporates a custom multi-task
longformer encoder decoder to summarize longer documents. Interactive
visualizations of document concepts as a network reflecting both semantic
relatedness and co-occurrence help users focus on concepts of interest. The
user can select these concepts and automatically update the summary to
emphasize them. We present two iterations of ConceptEVA evaluated through an
expert review and a within-subjects study. We find that participants'
satisfaction with customized summaries through ConceptEVA is higher than their
own manually-generated summary, while incorporating critique into the summaries
proved challenging. Based on our findings, we make recommendations for
designing summarization systems incorporating mixed-initiative interactions.Comment: 16 pages, 7 figure