Explainable News Summarization -- Analysis and mitigation of Disagreement Problem

Abstract: Explainable AI (XAI) techniques for text summarization provide valuable understanding of how the summaries are generated. Recent studies have highlighted a major challenge in this area, known as the disagreement problem. This problem occurs when different XAI methods offer contradictory explanations for the summary generated from the same input article. This inconsistency across XAI methods has been evaluated using predefined metrics designed to quantify agreement levels between them, revealing significant disagreement. This impedes the reliability and interpretability of XAI in this area. To address this challenge, we propose a novel approach that utilizes sentence transformers and the k-means clustering algorithm to first segment the input article and then generate the explanation of the summary generated for each segment. By producing regional or segmented explanations rather than comprehensive ones, a decrease in the observed disagreement between XAI methods is hypothesized. This segmentation-based approach was used on two news summarization datasets, namely Extreme Summarization(XSum) and CNN-DailyMail, and the experiment was conducted using multiple disagreement metrics. Our experiments validate the hypothesis by showing a significant reduction in disagreement among different XAI methods. Additionally, a JavaScript visualization tool is developed, that is easy to use and allows users to interactively explore the color-coded visualization of the input article and the machine-generated summary based on the attribution scores of each sentences.