Exploring Iterative Controllable Summarization with Large Language Models

AbstractLarge language models (LLMs) have demonstrated remarkable performance in abstractive summarization tasks. However, their ability to precisely control summary attributes (e.g., length or topic) remains underexplored, limiting their adaptability to specific user preferences. In this paper, we systematically explore the controllability of LLMs. To this end, we revisit summary attribute measurements and introduce iterative evaluation metrics, failure rate and average iteration count, to more precisely evaluate controllability beyond assessment of errors. Our findings show that LLMs struggle more with numerical attributes than with linguistic attributes. To address this challenge, we propose a guide-to-explain framework (GTE) for controllable summarization. GTE enables the model to identify misaligned attributes in the initial draft and guides it to self-explain errors in the previous output. By encouraging reflection on attribute misalignment, GTE generates well-adjusted summaries that satisfy the desired attributes with robust effectiveness while requiring surprisingly fewer iterations than other iterative approaches.