mlsanityData to 14 Oct 2026

Revision for Concision: A Constrained Paraphrase Generation Task

arXiv 2210.14257 ↗First posted 25 Oct 2022cs.CLcited by 4

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Abstract (arXiv)

Academic writing should be concise as concise sentences better keep the readers' attention and convey meaning clearly. Writing concisely is challenging, for writers often struggle to revise their drafts. We introduce and formulate revising for concision as a natural language processing task at the sentence level. Revising for concision requires algorithms to use only necessary words to rewrite a sentence while preserving its meaning. The revised sentence should be evaluated according to its word choice, sentence structure, and organization. The revised sentence also needs to fulfil semantic retention and syntactic soundness. To aide these efforts, we curate and make available a benchmark parallel dataset that can depict revising for concision. The dataset contains 536 pairs of sentences before and after revising, and all pairs are collected from college writing centres. We also present and evaluate the approaches to this problem, which may assist researchers in this area.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 31 of its 69 references that are in the map. First posted on arXiv 25 Oct 2022.

Builds on

31 of 69 references.

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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 1; 2025: 0; 2026: 1.

4 papers.

  1. 2023Predicting the Quality of Revisions in Argumentative Writingcited by 7
  2. 2023Predicting Desirable Revisions of Evidence and Reasoning in Argumentative Writingcited by 6
  3. 2024From Intents to Conversations: Generating Intent-Driven Dialogues with Contrastive Learning for Multi-Turn Classificationcited by 2
  4. 2026Rendering Data Unlearnable by Exploiting LLM Alignment Mechanismscited by 1