The task of rumour verification in social media concerns assessing the
veracity of a claim on the basis of conversation threads that result from it.
While previous work has focused on predicting a veracity label, here we
reformulate the task to generate model-centric, free-text explanations of a
rumour's veracity. We follow an unsupervised approach by first utilising
post-hoc explainability methods to score the most important posts within a
thread and then we use these posts to generate informative explanatory
summaries by employing template-guided summarisation. To evaluate the
informativeness of the explanatory summaries, we exploit the few-shot learning
capabilities of a large language model (LLM). Our experiments show that LLMs
can have similar agreement to humans in evaluating summaries. Importantly, we
show that explanatory abstractive summaries are more informative and better
reflect the predicted rumour veracity than just using the highest ranking posts
in the thread.

社交媒体上的谣言验证任务涉及根据产生的会话线程来评估主张的真实性。我们通过使用事后可解释方法评分来得到最重要的帖子，然后使用这些帖子通过模板引导总结生成信息性的解释总结，表明解释的抽象总结比仅使用线程中排名最高的帖子更具信息性并更好地反映了预测的谣言真实性，从而重新制定此任务。