Automatic headline generation enables users to comprehend ongoing news events promptly and has recently become an important task in web mining and natural language processing. With the growing need for news headline generation, we argue that the hallucination issue, namely the generated headlines being not supported by the original news stories, is a critical challenge for the deployment of this feature in web-scale systems Meanwhile, due to the infrequency of hallucination cases and the requirement of careful reading for raters to reach the correct consensus, it is difficult to acquire a large dataset for training a model to detect such hallucinations through human curation. In this work, we present a new framework named ExHalder to address this challenge for headline hallucination detection. ExHalder adapts the knowledge from public natural language inference datasets into the news domain and learns to generate natural language sentences to explain the hallucination detection results. To evaluate the model performance, we carefully collect a dataset with more than six thousand labeled <article, headline> pairs. Extensive experiments on this dataset and another six public ones demonstrate that ExHalder can identify hallucinated headlines accurately and justifies its predictions with human-readable natural language explanations.

ExHalder是一个用于检测标题幻觉的新框架, 通过将公共自然语言推理数据集的知识转化到新闻领域，并学习生成自然语言句子来解释幻觉检测结果，成功解决了标题生成中的幻觉问题。通过超过6000个标记的文章标题对，和另外6个公开数据集的广泛实验表明，ExHalder能够准确地识别幻觉，并用人类可读的自然语言解释来证明其预测结果。

使用解释来检测新闻标题中的幻觉现象