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.

翻译：采用最先进的自然语言处理和人工智能方法，为不同领域的读者生成有效的长文档多主题摘要仍然是一个挑战。为了解决这个问题，我们引入了ConceptEVA，这是一种生成、评估和定制长文档多主题摘要的混合式交互方法。ConceptEVA 采用一个自定义的多任务长式编码器解码器来摘要更长的文档。以反映语义相关性和共现性的文档概念网络的交互式可视化帮助用户关注感兴趣的概念。用户可以选择这些概念并自动更新摘要以强调它们。我们通过专家评论和一项研究进行了ConceptEVA的两次迭代评估。我们发现，参与者对通过ConceptEVA定制的摘要的满意度高于他们自己手动生成的摘要，而将批评纳入摘要则证明是具有挑战性的。根据我们的研究结果，我们推荐设计摘要系统，其中包括混合式交互作用。