Guide-to-Explain for Controllable Summarization

• URL: http://arxiv.org/abs/2411.12460v1
• Date: Tue, 19 Nov 2024 12:36:02 GMT
• Title: Guide-to-Explain for Controllable Summarization
• Authors: Sangwon Ryu, Heejin Do, Daehee Kim, Yunsu Kim, Gary Geunbae Lee, Jungseul Ok,
• Abstract summary: controllable summarization with large language models (LLMs) remains underexplored. We propose a guide-to-explain framework (GTE) for controllable summarization. Our framework enables the model to identify misaligned attributes in the initial draft and guides it in explaining errors in the previous output.
• Score: 11.904090197598505
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, large language models (LLMs) have demonstrated remarkable performance in abstractive summarization tasks. However, controllable summarization with LLMs remains underexplored, limiting their ability to generate summaries that align with specific user preferences. In this paper, we first investigate the capability of LLMs to control diverse attributes, revealing that they encounter greater challenges with numerical attributes, such as length and extractiveness, compared to linguistic attributes. To address this challenge, we propose a guide-to-explain framework (GTE) for controllable summarization. Our GTE framework enables the model to identify misaligned attributes in the initial draft and guides it in explaining errors in the previous output. Based on this reflection, the model generates a well-adjusted summary. As a result, by allowing the model to reflect on its misalignment, we generate summaries that satisfy the desired attributes in surprisingly fewer iterations than other iterative methods solely using LLMs.

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