BookAsSumQA: An Evaluation Framework for Aspect-Based Book Summarization via Question Answering

TL;DR

This paper introduces BookAsSumQA, a novel QA-based evaluation framework for aspect-based book summarization that effectively assesses summary quality across different text lengths using a narrative knowledge graph.

Contribution

It presents a new evaluation method for aspect-based book summaries leveraging question answering and a narrative knowledge graph, addressing challenges with long texts.

Findings

LLM-based approaches excel on shorter texts.

RAG-based methods outperform on longer documents.

The framework enables practical evaluation of book summaries.

Abstract

Aspect-based summarization aims to generate summaries that highlight specific aspects of a text, enabling more personalized and targeted summaries. However, its application to books remains unexplored due to the difficulty of constructing reference summaries for long text. To address this challenge, we propose BookAsSumQA, a QA-based evaluation framework for aspect-based book summarization. BookAsSumQA automatically generates aspect-specific QA pairs from a narrative knowledge graph to evaluate summary quality based on its question-answering performance. Our experiments using BookAsSumQA revealed that while LLM-based approaches showed higher accuracy on shorter texts, RAG-based methods become more effective as document length increases, making them more efficient and practical for aspect-based book summarization.