Memory-efficient large language models are good at refining text input for
better readability. However, controllability is a matter of concern when it
comes to text generation tasks with long inputs, such as multi-document
summarization. In this work, we investigate for a generic controllable approach
for multi-document summarization that leverages the capabilities of LLMs to
refine the text. In particular, we train a controllable content extraction
scheme to extract the text that will be refined by an LLM. The scheme is
designed with a novel coverage and coherence intuitive policy, which is duly
rewarded by a passively trained LLM. Our approach yields competitive results in
the evaluation using ROUGE metrics and outperforms potential baselines in
coherence, as per human evaluation.

使用内存高效的大型语言模型进行文本精炼有助于提高可阅读性，而在长文本输入的文本生成任务中，如多文档摘要，控制性是一个需要关注的问题。本文研究了一种用于多文档摘要的通用的可控方法，利用大型语言模型来提炼文本。具体来说，我们训练了一个可控的内容提取方案，用于提取需要由大型语言模型提炼的文本。该方案采用了一种新颖的覆盖和连贯性直观策略，并由一个被动训练的大型语言模型适当地奖励。我们的方法在使用 ROUGE 指标进行评估时取得了有竞争力的结果，并在人工评估中在连贯性方面胜过潜在的基准。