Extractive summarization can produce faithful summaries but often requires
additional constraints such as a desired summary length. Traditional sentence
compression models do not typically consider the constraints because of their
restricted model abilities, which require model modifications for coping with
them. To bridge this gap, we propose Instruction-based Compression
(InstructCMP), an approach to the sentence compression task that can consider
the length constraint through instructions by leveraging the zero-shot
task-solving abilities of Large Language Models (LLMs). For this purpose, we
created new evaluation datasets by transforming traditional sentence
compression datasets into an instruction format. By using the datasets, we
first reveal that the current LLMs still face challenges in accurately
controlling the length for a compressed text. To address this issue, we propose
an approach named "length priming," that incorporates additional length
information into the instructions without external resources. While the length
priming effectively works in a zero-shot setting, a training dataset with the
instructions would further improve the ability of length control. Thus, we
additionally created a training dataset in an instruction format to fine-tune
the model on it. Experimental results and analysis show that applying the
length priming significantly improves performances of InstructCMP in both
zero-shot and fine-tuning settings without the need of any model modifications.

传统句子压缩模型通常在考虑约束时面临能力限制，为弥补这一差距，我们提出了基于指令的压缩方法，利用大型语言模型的零 - shot 任务解决能力通过指令考虑长度约束。通过转换传统句子压缩数据集，我们发现目前的大型语言模型在控制压缩文本长度方面仍面临挑战，为解决这一问题，我们提出了一种称为 “长度提示” 的方法，将附加长度信息无需外部资源地融入指令中。实验结果和分析表明，应用长度提示显著提高了基于指令的压缩方法在零 - shot 和微调设置中的性能，而无需进行任何模型修改。