S umma C oz: A Dataset for Improving the Interpretability of Factual Consistency Detection for Summarization (2024)

Abstract: Summarization is an important application of Large Language Models (LLMs). When judging the quality of a summary, factual consistency holds a significant weight. Despite numerous efforts dedicated to building factual inconsistency detectors, the exploration of explanability remains limited among existing effort. In this study, we incorporate both human-annotated and model-generated natural language explanations elucidating how a summary deviates and thus becomes inconsistent with its source article. We build our explanation-augmented dataset on top of the widely used SummaC summarization consistency benchmark. Additionally, we develop an inconsistency detector that is jointly trained with the collected explanations. Our findings demonstrate that integrating explanations during training not only enables the model to provide rationales for its judgments but also enhances its accuracy significantly.

## introduction factual consistency checking in summarization assesses whether the information presented in a machine-generated summary aligns, and thus is consistent, with its source document(s). this task has gained prominence in recent years due to concerns about abstractive summarization systems generating erroneous or "hallucinated" content and thus compromising their reliability @xcite . traditionally, the task of factual consistency checking has been formulated as a binary classification problem, where the output label indicates whether the summary is consistent or not. however, a binary label alone offers limited insights into the nature of inconsistencies. when an inconsistency is identified, it would be better to pinpoint which part of the summary is inconsistent, cite corresponding information from the source * forrest bao is now with funix.io document, and explain the differences between the summary and the source. this explanatory information serves as valuable guidance for manually or automatically post-editing the summary to rewrite and rectify any inconsistencies @xcite . current research focuses on detecting inconsistencies in summaries without delving into explanations . approaches such as mfma @xcite , falsesumm @xcite , and nonfacts @xcite employ entailment classification methods that only yield binary classification outputs. there have been a few datasets @xcite ) that annotate the spans in the summaries that are inconsistent to their respective sources.but the information or text spans in the source documents that correspond to and falsify such inconsistent spans are missing in these datasets. in this paper, to facilitate the research in summarization consistency, we curate a dataset that includes not only the binary consistency labels but also natural language explanations as to why a summary is inconsistent to its source. to do so, we extend the summac @xcite summarization consistency benchmark by augmenting its binary labels with both human-annotated and llm-generated explanations. the resulting dataset, called summacoz, not only adds interpretablity into factual consistency evaluation but also, as to be shown later in this paper, sheds lights on the challenges of detecting inconsistent summaries. with summacoz foot_0 , we then train a text generation model that serves as not only a classifier but also a reasoner that justifies its classification judgement. empirical evaluation demonstrates that leveraging explanations during training results in a factual consistency detector not only adds interpretability to it but also makes it more accurate. ## curating the summacoz dataset the summacoz dataset is curated (figure 1 ) in a semi-automatic manner followed by rigorous quality check. the curation began with inconsistent summaries and their corresponding sources from the validation split of the summac summarization benchmark @xcite . next, we employed a large language model (llm) to generate an initial explanation for each of such inconsistent summaries as to why it deviates from its source. the llm-generated explanation is subsequently refined by human annotators. finally, to ensure the quality of the explanations, we cross-check the human-edited annotations with a more advanced llm and exclude samples where the human annotators and the advanced llm disagree. we focus on inconsistent summaries only in summacoz for several reasons. first, recognizing consistent summaries is relatively easy. chatgpt can accurately recognize over 95% of consistent summaries @xcite . second, it's challenging to pinpoint why a summary is supported by its corresponding article. hence, this study skips consistent summaries. our effort is focused on understanding and addressing the nuances and challenges associated with identifying inconsistent summaries. ## experiments we conduct a series of experiments to assess the effectiveness of utilizing natural language explanations in building a factual consistency detector. through these experiments, we aim to evaluate the performance our approach in providing interpretable explanations for summarization factual inconsistencies. ## conclusion in this study, we explore the integration of explanations into the inconsistency detector, with the goal of offering insights into the underlying processes driving evaluation outcomes. to achieve this, we compile a dataset named summacoz, comprising both human-written and llm-generated natural language explanations for inconsistent summary and article pairs. leveraging this dataset, we showcase the effectiveness of training a text-generation model to output both the consistency judgment and explanation simultaneously. this approach empowers users to understand and interpret evaluation results effectively, thereby enhancing transparency and trust in the assessment process.