Abstract
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.
Abstract (translated)
文本摘要在自然语言处理（NLP）社区中是一项成熟的任务。然而，针对用户需求的可控性摘要只是最近才受到关注。尽管有几个研究探索了文本摘要中的可控性问题，但对于多属性可控性摘要（MACS）的研究仍然有限。这项工作通过从大型语言模型（LLMs）的角度来考察MACS任务，并使用各种学习范式，特别是低秩适配器，解决了这一空白。我们实验了几种流行的适配器微调策略，以评估生成的模型在保留与多个可控属性相关的线索和模式方面的效果。此外，我们提出并评估了一种新颖的层次化适配器融合技术，用于整合来自两个不同可控属性的学习成果。随后，我们将展示我们的发现、讨论遇到的挑战，并建议推进MACS任务的可能性途径。
URL
https://arxiv.org/abs/2411.01213
