Google Research

Conciseness: An Overlooked Language Task

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP), Association for Computational Linguistics, Abu Dhabi

Abstract

We report on novel investigations into training models that make sentences concise. We define the task and show that it is different from related tasks such as summarization and simplification. For evaluation, we release two test sets, consisting of 2000 sentences each, that were annotated by two and five raters, respectively. We demonstrate that conciseness is a difficult task for which zero-shot setups with giant neural language models often do not perform well. Given the limitations of these approaches, we propose a synthetic data generation method based on round-trip translations. Using this data to either train Transformers from scratch or fine-tune T5 models yields our strongest baselines that can be further improved by fine-tuning on an artificial conciseness dataset that we derived from multi-annotator machine translation test sets.

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