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

• URL: http://arxiv.org/abs/2310.03473v1
• Date: Thu, 5 Oct 2023 11:29:09 GMT
• Title: Controllable Multi-document Summarization: Coverage & Coherence Intuitive Policy with Large Language Model Based Rewards
• Authors: Litton J Kurisinkel, Nancy F chen
• Abstract summary: Controllability is a matter of concern when it comes to text generation tasks with long inputs, such as multi-document summarization. We train a controllable content extraction scheme to extract the text that will be refined by an LLM. Our approach yields competitive results in the evaluation using ROUGE metrics and outperforms potential baselines in coherence.
• Score: 42.171703872560286
• License: http://creativecommons.org/licenses/by/4.0/
• 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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