Controlled Text Reduction

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 5699 - 5715 December 7-11, 2022 ©2022 Association for Computational Linguistics Controlled Text Reduction Aviv Slobodkin, Paul Roit, Eran Hirsch, Ori Ernst, Ido Dagan Bar-Ilan University {lovodkin93,plroit,hirsch.eran,oriern}@gmail.com dagan@cs.biu.ac.il Abstract Producing a reduced version of a source text, as in generic or focused summarization, in- herently involves two distinct subtasks: decid- ing on targeted content and generating a co- herent text conveying it. While some popu- lar approaches address summarization as a sin- gle 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 tar- geted 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 Controlled Text Reduction as a standalone task, whose in- put is a source text with marked spans of tar- geted content ("highlighting"). A model then needs to generate a coherent text that includes all and only the target information. We advo- cate the potential of such models, both for mod- ular fully-automatic summarization, as well as for semi-automated human-in-the-loop use cases. Facilitating proper research, we crowd- source high-quality dev and test datasets for the task. Further, we automatically generate a larger "silver" training dataset from available summarization benchmarks, leveraging a pre- trained summary-source alignment model. Fi- nally, employing these datasets, we present a supervised baseline model, showing promising results and insightful analyses.1 1 Introduction Abstractive text summarization takes one or more documents as input and aims at generating an accu- rate and coherent summary from it. It requires both locating salient information in the input and then 1Our data and code are released for open access: https://huggingface.co/datasets/biu-nlp/ Controlled-Text-Reduction-dataset https://github.com/lovodkin93/Controlled_Text_ Reduction generating a concise text covering it. While some modern state-of-the-art abstractive summarization models treat the task as a single end-to-end task, it has been common practice for summarization mod- els to separate the salience detection phase from the text generation phase (Barzilay and McKeown, 2005; Oya et al., 2014; Banerjee et al., 2016; Vilca and Cabezudo, 2017), with renewed popularity in recent years (Lebanoff et al., 2019, 2020a,b; Xiao et al., 2022; Ernst et al., 2021a; Gehrmann et al., 2018a; Chen and Bansal, 2018; Cho et al., 2019). But, though those proposed techniques comprised distinguishable subtasks, evaluation was performed on the whole summarization pipeline, rather than optimizing each step separately. In this paper, we focus on the text generation step, while addressing it as a standalone task at the sub-sentence level. To that end, we introduce a new task which we denote Controlled Text Reduction. The task takes as input a document with pre-chosen salient spans in it, which we will henceforth call highlights. A model is then expected to reduce the document to a smaller coherent text which covers all and only the highlighted content, i.e., consoli- dating the highlighted spans into a fluent and coher- ent passage, as exemplified in …

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