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.
@article{arxiv.2310.03473,
title = {Controllable Multi-document Summarization: Coverage & Coherence Intuitive Policy with Large Language Model Based Rewards},
author = {Litton J Kurisinkel and Nancy F chen},
journal= {arXiv preprint arXiv:2310.03473},
year = {2023}
}