  Current approaches for text summarization are predominantly automatic, with
rather limited space for human intervention and control over the process. In
this paper, we introduce SummHelper, a 2-phase summarization assistant designed
to foster human-machine collaboration. The initial phase involves content
selection, where the system recommends potential content, allowing users to
accept, modify, or introduce additional selections. The subsequent phase,
content consolidation, involves SummHelper generating a coherent summary from
these selections, which users can then refine using visual mappings between the
summary and the source text. Small-scale user studies reveal the effectiveness
of our application, with participants being especially appreciative of the
balance between automated guidance and opportunities for personal input.
