Controlled Text Reduction

• URL: http://arxiv.org/abs/2210.13449v1
• Date: Mon, 24 Oct 2022 17:59:03 GMT
• Title: Controlled Text Reduction
• Authors: Aviv Slobodkin, Paul Roit, Eran Hirsch, Ori Ernst, Ido Dagan
• Abstract summary: We formalize textitControlled Text Reduction as a standalone task. A model then needs to generate a coherent text that includes all and only the target information.
• Score: 15.102190738450092
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Producing a reduced version of a source text, as in generic or focused summarization, inherently involves two distinct subtasks: deciding on targeted content and generating a coherent text conveying it. While some popular approaches address summarization as a single end-to-end task, prominent works support decomposed modeling for individual subtasks. Further, semi-automated text reduction is also very appealing, where users may identify targeted content while models would generate a corresponding coherent summary. In this paper, we focus on the second subtask, of generating coherent text given pre-selected content. Concretely, we formalize \textit{Controlled Text Reduction} as a standalone task, whose input is a source text with marked spans of targeted content ("highlighting"). A model then needs to generate a coherent text that includes all and only the target information. We advocate the potential of such models, both for modular fully-automatic summarization, as well as for semi-automated human-in-the-loop use cases. Facilitating proper research, we crowdsource high-quality dev and test datasets for the task. Further, we automatically generate a larger "silver" training dataset from available summarization benchmarks, leveraging a pretrained summary-source alignment model. Finally, employing these datasets, we present a supervised baseline model, showing promising results and insightful analyses.

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