结合提示信号与图结构的对话摘要生成模型

doi:10.3778/j.issn.1002-8331.2405-0101

摘要/Abstract

摘要： 以对话形式为主的通信方式逐渐普及，对话摘要任务引起越来越多研究者的关注，该任务旨在将复杂的对话文本压缩成简洁的表示形式。在对话文本中，多个对话者之间的交流通常涉及有关某个特定事件的关键信息，且这些信息分布较为分散。然而，现有方法未深入研究对话内容的内在关系和结构，容易遗漏关键信息。针对上述问题，设计了结合提示信号与图结构的对话摘要生成模型，旨在帮助理解对话结构并把握对话中的关键信息，进而提高摘要生成的准确率。该模型基于提示学习引入了离散提示信号，并将其输入提示编码器，旨在利用提示信号协助模型更有针对性地聚焦对话的关键信息（关键词、主题词等）。同时，该模型引入动态图结构，旨在利用对话的结构性信息来捕捉并整合跨句子信息。在SAMSum、QMsum和DialogSum数据集上的实验结果表明，ROUGE-1、ROUGE-2和ROUGE-L得分均取得了显著提升，验证了模型的有效性。

关键词: 对话摘要, 提示学习, 提示信号, 图结构

Abstract: With the increasing popularity of dialog-based communication, more and more researchers pay attention to the task of dialogue summarization, which aims to compress the complex dialogue text into a concise representation. In a dialogue text, the communication between multiple roles usually involves key information about a particular event and this information is distributed in several conversations. However, the existing methods cannot deeply study the internal relationship and structure of dialogue text, and it is easy to miss key information. To solve these problems, a dialogue summarization model combining prompt signal and graph structure is designed to help understand the dialogue structure and grasp the key information, so as to improve the accuracy of the summary. Based on prompt learning, discrete prompt signals are introduced into the prompt encoder to help the model focus on key information (keywords, topics, etc. ) more pertinently. At the same time, the dynamic graph structure is introduced to capture and integrate cross-sentence information by using the structural information of dialogue. Experimental results on SAMSum, QMsum and DialogSum datasets show that the ROUGE-1, ROUGE-2 and ROUGE-L scores are significantly improved, which verifies the effectiveness of the model proposed in this paper.

Key words: dialogue summarization, prompt learning, prompt signal, graph structure

金彦亮, 冯湫燕, 高塬. 结合提示信号与图结构的对话摘要生成模型[J]. 计算机工程与应用, 2025, 61(15): 241-250.

JIN Yanliang, FENG Qiuyan, GAO Yuan. Dialogue Summarization Combining Prompt Signal and Graph Structure[J]. Computer Engineering and Applications, 2025, 61(15): 241-250.

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