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：尽管最先进的自然语言处理与人工智能方法已能高效处理长文本和多主题文档（如学术论文），但面向不同领域读者生成有效摘要仍具挑战性。为此，我们提出ConceptEVA——一种混合主动性方法，用于长文本多主题文档的摘要生成、评估与定制化。该框架集成了自定义多任务长编码器-解码器（Longformer Encoder-Decoder）以处理长文档，并通过交互式可视化将文档概念以反映语义关联性与共现关系的网络形式呈现，帮助用户聚焦感兴趣的概念。用户可选择这些概念并自动更新摘要以突出相关内容。我们通过专家评审和受试者内实验对ConceptEVA进行了两轮迭代评估。结果表明，参与者对通过ConceptEVA定制的摘要满意程度高于其手动生成的摘要，但将批评意见整合至摘要仍具挑战性。基于研究发现，我们提出了融合混合主动性交互的摘要系统设计建议。