Revisiting Sentence Union Generation as a Testbed for Text Consolidation

Eran Hirsch, Valentina Pyatkin, Ruben Wolhandler, Avi Caciularu, Asi Shefer, Ido Dagan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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.

Original languageEnglish
Title of host publicationFindings of the Association for Computational Linguistics, ACL 2023
PublisherAssociation for Computational Linguistics (ACL)
Number of pages21
ISBN (Electronic)9781959429623
StatePublished - 2023
Event61st Annual Meeting of the Association for Computational Linguistics, ACL 2023 - Toronto, Canada
Duration: 9 Jul 202314 Jul 2023

Publication series

NameProceedings of the Annual Meeting of the Association for Computational Linguistics
ISSN (Print)0736-587X


Conference61st Annual Meeting of the Association for Computational Linguistics, ACL 2023

Bibliographical note

Publisher Copyright:
© 2023 Association for Computational Linguistics.


The work described herein was supported in part by grants from One AI, the Israel Science Foundation 2827/21 and the Israel Ministry of Science and Technology. We would like to thank the workers who have annotated this dataset and we appreciate their dedication in ensuring a high level of quality. We express our gratitude to Dr. Kapil Thadani for assisting us in retrieving his data from an earlier research endeavor.

FundersFunder number
Israel Science Foundation2827/21
Ministry of science and technology, Israel


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