  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.
