Improving Faithfulness in Abstractive Summarization with Contrast Candidate Generation and Selection (2021)

Abstract: Despite significant progress in neural abstractive summarization, recent studies have shown that the current models are prone to generating summaries that are unfaithful to the original context. To address the issue, we study contrast candidate generation and selection as a model-agnostic post-processing technique to correct the extrinsic hallucinations (i.e. information not present in the source text) in unfaithful summaries. We learn a discriminative correction model by generating alternative candidate summaries where named entities and quantities in the generated summary are replaced with ones with compatible semantic types from the source document. This model is then used to select the best candidate as the final output summary. Our experiments and analysis across a number of neural summarization systems show that our proposed method is effective in identifying and correcting extrinsic hallucinations. We analyze the typical hallucination phenomenon by different types of neural summarization systems, in hope to provide insights for future work on the direction.

## introduction abstractive summarization is the task of producing a concise and fluent summary that is salient and faithful to the source document(s). datadriven, neural methods @xcite @xcite , and the more recent, pretrained transformer language models @xcite @xcite , have shown improvements in the fluency and salience of generated summaries. however, less progress has been made on improving the faithfulness of the generated summaries, that is, producing a summary that is entailed by the information presented in the source document. despite the increased level of performance under automatic metrics such as rouge * most of the work done while the authors were at google. source: he was re-elected for a second term by the un general assembly, unopposed and unanimously, on 21 june 2011, with effect from 1 january 2012. mr. ban describes his priorities as mobilising world leaders to deal with climate change, economic upheaval, pandemics and increasing pressures involving food, energy and water... unfaithful summary: the united nations secretary-general ban ki-moon was elected for a second term in 2007. our summary: the united nations secretary-general ban ki-moon was elected for a second term in 21 june 2011. in this work, we focus on the problem of correcting such hallucinations as a post processing step 1 . a post processing correction step allows us to rely on the fluency of the text generated by sota systems, that gain from huge pretrained models and large fine-tuning datasets, and correct it using small amounts of automatically generated training data. under the setting where a large fraction of ground truth summarization data is hallucinated, as we show in our main contributions are as follows. first, our work is the first to study the effectiveness of contrast candidate generation and selection as a model-agnostic method for correcting hallucinations, under the setting where a large fraction of ground truth summarization data suffers from hallucinations. second, we validate our method on various neural summarization systems trained on xsum, and provide detailed analysis on the typical types of hallucinations from each system. ## contrast candidate generation & selection our proposed method is built on the observation that a large fraction of extrinsic hallucinations happen on named entities and quantities. as @xcite further show that the hallucinations in training data translate to similar issues for the generated outputs across different summarization models, we want to study a modelagnostic, post-processing method that can correct such entity and quantity hallucinations. we frame the problem as a correction task and make it conceptually a less complex problem than summarization. modeling correction as a standalone task would type % ent. % num. % faithful 23.1 --ex. hallucination 73.1 35.9 18.2 in. hallucination 7.4 1.9 0.5 the "%" column shows the % of intrinsic and extrinsic hallucinations annotated by @xcite . we analyzed the % of hallucinations on entities and numbers/quantities, and show the % out of all 500 summaries in the right two columns. ## related work there have been growing interests in quantitatively measuring the faithfulness of text generation models. most widely-adopted evaluation metrics for text generation, such as rouge @xcite and bertscore @xcite , correlate poorly with the human perceived faithfulness of the generated text @xcite . recent studies explore categorical, contentbased analysis for measuring the faithfulness of summaries @xcite . narayan et al. (2018b); deutsch et al. (2020); durmus et al. (2020) propose to use question answering to test the consistency of summary content to the information presented in the source text. there have been efforts to study pre-or post-processing methods to improving faithfulness of generated summaries. @xcite attempt to use textual entailment models to re-rank the summary candidates generated from beam search or different neural systems. as @xcite highlight the existence of hallucinations in training data, truncating potentially unfaithful gold summaries during training is an effective strategy @xcite . @xcite take similar apporach as in this work to identify the hallucinations in summary. a concurrent study to this work @xcite uses similar strategies as in this paper on a dataset with a very small fraction of hallucinations present. our study instead focuses on the more challenging setting @xcite where a large part of training data suffers from extrinsic and intrinsic hallucinations, and provides cross-system analysis on the both hallucinations categories. ## conclusion we study contrast candidate generation and selection as a method to apply post-hoc fixes to extrinsically hallucinated summary on entities and quantities, under the setting where the summarization dataset suffers from intrinsic and extrinsic halluci-nations. we conduct our experiments on the xsum dataset, and show that our method is able to correct extrinsic hullucinations, but incurs a small fraction of intrinsic hallucinations on mistakes. we also provide detailed analysis and discussions on the capabilities and limitations of our method. we hope our findings in the paper will provide insights to future work in this direction.