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arXiv · 2310.03473

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

Litton J Kurisinkel·Nancy F chen
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Abstract

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.

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Litton J Kurisinkel, Nancy F chen. 2023-10-05. Controllable Multi-document Summarization: Coverage & Coherence Intuitive Policy with Large Language Model Based Rewards. https://arxiv.org/abs/2310.03473

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