On the Intractability to Synthesize Factual Inconsistencies in Summarization (2024)

Abstract: Factual consistency detection has gotten raised attention in the task of abstractive summarization. Many existing works rely on synthetic training data, which may not accurately reflect or match the inconsistencies produced by summarization models. In this paper, we first systematically analyze the shortcomings of the current methods in synthesizing inconsistent summaries. Current synthesis methods may fail to produce inconsistencies of coreference errors and discourse errors, per our quantitative and qualitative study. Then, employing the parameter-efficient finetuning (PEFT) technique, we discover that a competitive factual consistency detector can be achieved using thousands of real model-generated summaries with human annotations. Our study demonstrates the importance of real machine-generated texts with human annotation in NLG evaluation as our model outperforms the SOTA on the CoGenSumm, FactCC, Frank, and SummEval datasets.

## introduction with the advancements in neural conditioned generation, abstractive summarization systems, which are dominantly based on neural networks, have achieved phenomenal performances. however, summaries generated so often contain content that is factually inconsistent with the source documents @xcite and thus undermines the reliability and usability of the summaries. thus detecting factual inconsistencies is an important task associated with summarization. however, detecting inconsistencies in machinegenerated summaries is not trivial. due to the high labor cost of examining model-generated summaries, no existing datasets contain enough * no affiliation, currently working at apple inc. we believe that the clue to improve inconsistency detection lies in the inconsistent samples that the state of the art (sota) fails to detect. by analyzing such samples in the famous summac benchmark, we find that certain types of factual inconsistencies are hard to be synthesized and thus are uncovered in the training of sota. specifically, they are the coreference errors and discourse link errors defined by the frank dataset @xcite . a coreference error happens when a pronoun in the summary has a wrong referent than that in the document. a discourse error happens when the summary mistakenly mixes multiple statements in the document. these errors can occur in the summary when the source information is either in a single sentence or across multiple sentences. the intractability to synthesize the said inconsistent training samples motivates us to take a different route to build an inconsistency detector via efficient use of limited human annotations on machinegenerated summaries. thanks to the parameter-efficient fine-tuning (peft) methods, we manage to finetune only 0.14% of the 0.9b parameters of the deberta-v2-xlarge-mnli model using thousands of samples in the validation set of summac. our model outperforms the sota on the cogen-summ, factcc, frank, and summeval datasets. error rates in nearly all types of inconsistencies are improved by our approach. our code is available at https://github.com/ nkwbtb/factft . we organize the paper as follows: the sota inconsistency detectors trained with synthetic inconsistent summaries still have a huge room for improvement. for example, the balanced accuracy of mfma @xcite tops at 84.5% on six major inconsistency datasets. to propose an improvement, we argue that it is important to analyze the nature of factually inconsistent samples undetected by the sota detectors. in this section, we first theoretically analyze the gap between the inconsistencies synthesized by sota for training and the real inconsistencies in summaries generated by neural generative models. then we empirically study the gap using a case study on the summac benchmark with two sota approaches. ## factft: inconsistency detection using machine-generated summaries with human annotations given a source document @xmath0, d 1 , . . .] and a machine-generated summary @xmath1, s 1 , . . .], where d i or s i is a sentence, a factual consistency detector is a binary classifier predicting whether the summary is factually consistent with the document, i.e., f (d, s) ∈ {0, 1} where 0 and 1 represent inconsistent (negative) and consistent (positive). realizing the difficulty to cover the diverse errors synthetically (section 2), we directly train a factual consistency classifier using an nli model as the foundation and the currently available but limited machine-generated summaries with human annotations as the training data. the recent advances in parameter-efficient finetuning (peft) has made this approach feasible. ## conclusion to identify directions to improve the detection accuracy of summary factual consistency, we begin this study by examining the inconsistency synthesis methods used in sota summarization consistency detectors, both theoretically and empirically. we find that coreference errors and discourse errors are the two most difficult types of factual errors missed by sota consistency detectors trained with synthetic data because existing methods to synthesize inconsistencies may fail to produce them. realizing the diversity of inconsistencies and the challenges to mimic them by manually designed synthesis heuristics, we propose to use limited but actual machine-generated summaries with human annotation to parameter-efficiently finetune an nli model of 0.9b parameters. the finetuned classifier outperforms sota on four datasets. this finding highlights the importance of using real machinegenerated texts for building metrics for nlg. we hope our effort can encourage the community to build more and better summarization consistency datasets with unified taxonomy.