SummHelper: Collaborative Human-Computer Summarization
This addresses the need for more user-friendly and controllable summarization tools for people who require tailored summaries, though it is incremental in combining existing human-computer interaction concepts.
The paper tackles the problem of limited human control in automatic text summarization by introducing SummHelper, a 2-phase collaborative assistant that allows users to select and refine content, with small-scale user studies showing its effectiveness in balancing automation and personal input.
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.