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

Controllable Multi-document Summarization: Coverage & Coherence Intuitive Policy with Large Language Model Based Rewards