Revision for Concision: A Constrained Paraphrase Generation Task
This addresses the challenge of helping writers produce more concise academic text, though it is incremental as it formulates a new task without demonstrating major performance gains.
The paper introduces revising for concision as a sentence-level NLP task, where algorithms must rewrite sentences to be more concise while preserving meaning, and provides a benchmark dataset of 536 sentence pairs collected from college writing centers.
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.