In today's data and information-rich world, summarization techniques are essential in harnessing vast text to extract key information and enhance decision-making and efficiency. In particular, topic-focused summarization is important due to its ability to tailor content to specific aspects of an extended text. However, this usually requires extensive labelled datasets and considerable computational power. This study introduces a novel method, Augmented-Query Summarization (AQS), for topic-focused summarization without the need for extensive labelled datasets, leveraging query augmentation and hierarchical clustering. This approach facilitates the transferability of machine learning models to the task of summarization, circumventing the need for topic-specific training. Through real-world tests, our method demonstrates the ability to generate relevant and accurate summaries, showing its potential as a cost-effective solution in data-rich environments. This innovation paves the way for broader application and accessibility in the field of topic-focused summarization technology, offering a scalable, efficient method for personalized content extraction.

翻译：在当今数据与信息丰富的世界中，摘要技术对于驾驭海量文本、提取关键信息并增强决策效率至关重要。其中，主题聚焦摘要因其能针对长文本的特定方面定制内容而显得尤为重要。然而，这通常需要大量标注数据集和可观的计算资源。本研究提出一种名为“增强查询摘要”（Augmented-Query Summarization，AQS）的新方法，用于无需大量标注数据集的主题聚焦摘要生成，该方法利用查询增强与层次聚类技术。该技术框架使机器学习模型的可迁移性得以应用于摘要任务，从而规避了针对特定主题进行训练的需求。通过真实场景测试，我们的方法展现了生成相关且准确摘要的能力，凸显了其在数据丰富环境中作为经济高效解决方案的潜力。这一创新为主题聚焦摘要技术领域更广泛的应用与可及性开辟了道路，提供了一种可扩展且高效的个性化内容提取方法。