Revisiting Sentence Union Generation as a Testbed for Text Consolidation
This work addresses the challenge of properly evaluating models' ability to consolidate overlapping multi-text information, which is incremental by refining an existing task for clearer assessment.
The paper tackles the problem of assessing text consolidation capabilities in models by proposing sentence union generation as a well-defined testbed, decoupling it from subjective content selection, and they create the largest union dataset to date and evaluate state-of-the-art language models on it.
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.