  Tasks involving text generation based on multiple input texts, such as
multi-document summarization, long-form question answering and contemporary
dialogue applications, challenge models for their ability to properly
consolidate partly-overlapping multi-text information. However, these tasks
entangle the consolidation phase with the often subjective and ill-defined
content selection requirement, impeding proper assessment of models'
consolidation capabilities. In this paper, we suggest revisiting the sentence
union generation task as an effective well-defined testbed for assessing text
consolidation capabilities, decoupling the consolidation challenge from
subjective content selection. To support research on this task, we present
refined annotation methodology and tools for crowdsourcing sentence union,
create the largest union dataset to date and provide an analysis of its rich
coverage of various consolidation aspects. We then propose a comprehensive
evaluation protocol for union generation, including both human and automatic
evaluation. Finally, as baselines, we evaluate state-of-the-art language models
on the task, along with a detailed analysis of their capacity to address
multi-text consolidation challenges and their limitations.
