Explanatory Summarization with Discourse-Driven Planning
This work addresses the need for better automatic summarization tools that can produce explanations for lay readers, though it appears incremental as it builds on existing discourse frameworks.
The paper tackled the problem of generating lay summaries with appropriate explanatory content by introducing a discourse-driven planning approach, which outperformed existing state-of-the-art methods on three datasets in terms of summary quality, robustness, controllability, and reduced hallucination.
Lay summaries for scientific documents typically include explanations to help readers grasp sophisticated concepts or arguments. However, current automatic summarization methods do not explicitly model explanations, which makes it difficult to align the proportion of explanatory content with human-written summaries. In this paper, we present a plan-based approach that leverages discourse frameworks to organize summary generation and guide explanatory sentences by prompting responses to the plan. Specifically, we propose two discourse-driven planning strategies, where the plan is conditioned as part of the input or part of the output prefix, respectively. Empirical experiments on three lay summarization datasets show that our approach outperforms existing state-of-the-art methods in terms of summary quality, and it enhances model robustness, controllability, and mitigates hallucination.