用大模型实现多属性可控文本摘要，提升个性化生成能力。
One Arrow, Many Targets: Probing LLMs for Multi-Attribute Controllable Text Summarization
- 通过低秩适配器实现多属性控制，保持语义一致性。
- 提出分层适配器融合技术，有效整合双重控制属性。
- 为个性化摘要生成提供可扩展的技术路径，适合研究者参考。
文本摘要是自然语言处理领域的成熟任务，但针对用户需求的可控摘要研究近年才兴起。尽管已有工作探索摘要可控性，多属性可控摘要（MACS）仍缺乏深入研究。本文从大语言模型（LLM）视角出发，采用多种学习范式，尤其是低秩适配器，探究MACS任务。实验对比了不同主流适配器微调策略，评估模型在保留多个可控属性线索与模式方面的表现。此外，提出并验证一种新型分层适配器融合技术，以整合两种不同可控属性的学习成果。随后呈现研究发现，讨论遇到的挑战，并提出推动MACS任务发展的潜在方向。
原文摘要 · Abstract (English)
Text summarization is a well-established task within the natural language processing (NLP) community. However, the focus on controllable summarization tailored to user requirements is gaining traction only recently. While several efforts explore controllability in text summarization, the investigation of Multi-Attribute Controllable Summarization (MACS) remains limited. This work addresses this gap by examining the MACS task through the lens of large language models (LLMs), using various learning paradigms, particularly low-rank adapters. We experiment with different popular adapter fine-tuning strategies to assess the effectiveness of the resulting models in retaining cues and patterns associated with multiple controllable attributes. Additionally, we propose and evaluate a novel hierarchical adapter fusion technique to integrate learnings from two distinct controllable attributes. Subsquently, we present our findings, discuss the challenges encountered, and suggest potential avenues for advancing the MACS task.
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