Shortcomings of Question Answering Based Factuality Frameworks for Error Localization
This work highlights a critical limitation in error localization for summarization models, which is incremental as it identifies shortcomings in existing methods rather than proposing a new solution.
The paper evaluated the ability of question answering (QA)-based frameworks to localize factual errors in abstractive summaries and found they fail to correctly identify error spans, being outperformed by trivial exact match baselines.
Despite recent progress in abstractive summarization, models often generate summaries with factual errors. Numerous approaches to detect these errors have been proposed, the most popular of which are question answering (QA)-based factuality metrics. These have been shown to work well at predicting summary-level factuality and have potential to localize errors within summaries, but this latter capability has not been systematically evaluated in past research. In this paper, we conduct the first such analysis and find that, contrary to our expectations, QA-based frameworks fail to correctly identify error spans in generated summaries and are outperformed by trivial exact match baselines. Our analysis reveals a major reason for such poor localization: questions generated by the QG module often inherit errors from non-factual summaries which are then propagated further into downstream modules. Moreover, even human-in-the-loop question generation cannot easily offset these problems. Our experiments conclusively show that there exist fundamental issues with localization using the QA framework which cannot be fixed solely by stronger QA and QG models.