Title: Learning Argumentative Summarization with Iterative Rejection Sampling
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: summarization, language understanding, large language models, LLMs, domain-specific tasks, fine-tuning

Abstract: Summarization is a fundamental task for evaluating language understanding in both humans and machines, and serves as a crucial tool for information processing in our data-rich world. While large language models (LLMs) have shown significant progress in summarization, they still struggle with domain-specific tasks such as zero-shot medical documentation, legal text, and argumentative summarization. To improve argumentative text understanding and summarization, we propose an iterative fine-tuning framework that trains LLMs on high-quality argument-summary pairs generated by the model itself. These pairs are filtered using similarity scores calculated by comparing reconstructed arguments from summaries with the original arguments, using rejection sampling, without external supervision. Our experiments demonstrate that this method improves argument summarization performance, achieving gains up to 11.88% in BERT F1 similarity scores between reconstructed and original arguments, over the vanilla model without such fine-tuning on a dataset of 200 r/ChangeMyView posts.