Conventional dialogue summarization methods directly generate summaries and do not consider user's specific interests. This poses challenges in cases where the users are more focused on particular topics or aspects. With the advancement of instruction-finetuned language models, we introduce instruction-tuning to dialogues to expand the capability set of dialogue summarization models. To overcome the scarcity of instructive dialogue summarization data, we propose a three-step approach to synthesize high-quality query-based summarization triples. This process involves summary-anchored query generation, query filtering, and query-based summary generation. By training a unified model called InstructDS (Instructive Dialogue Summarization) on three summarization datasets with multi-purpose instructive triples, we expand the capability of dialogue summarization models. We evaluate our method on four datasets, including dialogue summarization and dialogue reading comprehension. Experimental results show that our approach outperforms the state-of-the-art models and even models with larger sizes. Additionally, our model exhibits higher generalizability and faithfulness, as confirmed by human subjective evaluations.

翻译：传统对话摘要方法直接生成摘要，未考虑用户的特定兴趣。当用户更关注特定主题或方面时，这一方法面临挑战。随着指令微调语言模型的发展，我们引入指令微调技术处理对话，以拓展对话摘要模型的能力集。针对指令式对话摘要数据稀缺的问题，我们提出一种三步法来合成高质量基于查询的摘要三元组：包括摘要锚定查询生成、查询过滤和基于查询的摘要生成。通过在三个摘要数据集上训练包含多用途指令三元组的统一模型InstructDS（指令式对话摘要），我们扩展了对话摘要模型的能力。我们在四个数据集上评估该方法，涵盖对话摘要和对话阅读理解任务。实验结果表明，我们的方法优于现有最优模型，甚至超越参数规模更大的模型。此外，人工主观评价证实，我们的模型展现出更强的泛化性和忠实性。