Label-Free Topic-Focused Summarization Using Query Augmentation

TL;DR

This paper presents AQS, a novel, label-free method for topic-focused summarization that uses query augmentation and hierarchical clustering to generate relevant summaries without extensive training data.

Contribution

The study introduces AQS, a transfer learning approach that enables topic-focused summarization without large labeled datasets, improving scalability and accessibility.

Findings

Demonstrates effective summary relevance and accuracy in real-world tests.

Shows potential for cost-effective, scalable summarization solutions.

Facilitates transferability of models to new summarization tasks.

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.