On Improving Summarization Factual Consistency from Natural Language Feedback (2023)

Abstract: Despite the recent progress in language generation models, their outputs may not always meet user expectations. In this work, we study whether informational feedback in natural language can be leveraged to improve generation quality and user preference alignment. To this end, we consider factual consistency in summarization, the quality that the summary should only contain information supported by the input documents, as the user-expected preference. We collect a high-quality dataset, DeFacto, containing human demonstrations and informational natural language feedback consisting of corrective instructions, edited summaries, and explanations with respect to the factual consistency of the summary. Using our dataset, we study three natural language generation tasks: (1) editing a summary by following the human feedback, (2) generating human feedback for editing the original summary, and (3) revising the initial summary to correct factual errors by generating both the human feedback and edited summary. We show that DeFacto can provide factually consistent human-edited summaries and further insights into summarization factual consistency thanks to its informational natural language feedback. We further demonstrate that fine-tuned language models can leverage our dataset to improve the summary factual consistency, while large language models lack the zero-shot learning ability in our proposed tasks that require controllable text generation.

## introduction while recent natural language generation (nlg) models @xcite @xcite have made significant progress on the generation quality, they cannot always generate outputs that meet the user needs. for example, while state-of-the-art summarization systems can generate fluent and relevant figure 1 : three nlg tasks studied using our dataset. the editing model aims to improve the initial systemgenerated summary given human feedback. the critic model aims to predict human feedback according to a user-required quality. the editor model aims to automatically correct factual errors by predicting both the human feedback and edited summary. such failures in satisfying the user needs have an intrinsic reason -the large benchmark datasets that are used to train nlg models are usually not collected according to pre-defined user needs, which results in a discrepancy between model behaviors and user expectations. for example, xsum @xcite , one of the most commonly used summarization datasets, contains a large portion of reference summaries with hallucinations. 2 as a result, summarization models trained on xsum dataset generate many non-factual the annotator provided an explanation about why the initial summary is not factually consistent, evidence (i.e., a sentence in the input document) to support their claims, instructions on how to correct the summary, and an edited summary (a demonstration) without the factual error. instead of aligning an existing nlg model to a specific user need, we explore adjusting model outputs according to the user needs through human demonstrations and feedback. specifically, we investigate three scenarios (fig. 1 ): (1) an editing model that aligns initial system outputs to human demonstrations based on the user feedback; (2) a critic model that predicts user feedback of initial system outputs according to the user requirements; we choose factual consistency of systemgenerated summaries as the user-required quality to study the aforementioned application scenarios. to this end, we collect a high-quality, informational dataset containing human demonstrations and feedback. specifically, the annotators are presented with initial system-generated summaries and asked to make changes to the summaries to make them factually consistent if they find errors in them. apart from the human-edited, factually consistent summaries, the annotators are also required to provide instructions on how to change the initial summaries (i.e., if they find errors in them) and explanation on why the initial summaries are factually consistent or not. an example of our dataset is shown in fig. 2 . using the collected dataset, we show that (1) the editing model can effectively leverage human feedback to adjust the initial sys-tem outputs towards human demonstrations; (2) the critic model can learn to generate meaningful feedback that can be used by the editing model; (3) the editor model can automatically correct factuality errors without explicit human intervention. moreover, we find that the editor model achieves better performance than the baseline model that only generates the edited summary, which indicates that natural language feedback can be beneficial for training models for the corresponding task. our contributions can be briefly summarized as: (1) we collect defacto, foot_2 a high-quality dataset containing human demonstrations and feedback for improving factual consistency of text summarization; (2) we conduct comprehensive analyses on the collected dataset, which provides further insights about factual consistency in text summarization, such as the relation between the type of factual errors and the type of editing operations; ## the defacto dataset our dataset, defacto, contains human demonstrations and feedback w.r.t. the factual consistency of system-generated summaries. we choose xsum dataset as the target dataset to conduct the data collection because it is the most commonly studied dataset for summarization factual consistency. for the system-generated summaries, we select pega-sus @xcite , a top-performing summarization model to generate summaries on both the validation and test set of the xsum dataset. ## defacto analysis with the collected annotations, we further split the data collected on the validation set of xsum dataset into a training set and a validation set for the following experiments. we perform data analyses with different aspects of the collected dataset. the basic dataset statistics are in tab. 1. out of all the examples, 71.1% of them contain at least one factual error, 58.8% of them contain extrinsic errors, 22.0% of them contain intrinsic errors, and 9.63% of them contain both types of errors. ## summary editing for improving factual consistency with defacto, we propose a new nlg task: editing the initial summary based on human feedback.