Abstract
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.
Abstract (translated)
在当今数据和信息丰富的世界中，总结技术是提取大量文本的关键，以提取关键信息和提高决策和效率。特别是，面向主题的总结对将内容定制到扩展文本的特定方面非常重要。然而，这通常需要大量的标记数据集和相当大的计算能力。本研究介绍了一种新颖的方法，自适应查询摘要（AQS），用于不需要大量标记数据集的主题集中。它利用查询增强和层次聚类。这种方法促进了机器学习模型在摘要任务上的可迁移性，绕过了主题特定训练的需求。通过现实世界的测试，我们的方法证明了生成相关且准确的摘要的能力，表明其作为一个经济高效解决方案在数据丰富的环境中的潜力。这一创新为主题集中摘要技术的更广泛应用和可访问性铺平了道路，为个人内容提取提供了一种可扩展、高效的规模方法。
URL
https://arxiv.org/abs/2404.16411
