S umma C : Re-Visiting NLI -based Models for Inconsistency Detection in Summarization (2022)

Abstract: In the summarization domain, a key requirement for summaries is to be factually consistent with the input document. Previous work has found that natural language inference (NLI) models do not perform competitively when applied to inconsistency detection. In this work, we revisit the use of NLI for inconsistency detection, finding that past work suffered from a mismatch in input granularity between NLI datasets (sentence-level), and inconsistency detection (document level). We provide a highly effective and light-weight method called SummaCConv that enables NLI models to be successfully used for this task by segmenting documents into sentence units and aggregating scores between pairs of sentences. We furthermore introduce a new benchmark called SummaC (Summary Consistency) which consists of six large inconsistency detection datasets. On this dataset, SummaCConv obtains state-of-the-art results with a balanced accuracy of 74.4%, a 5% improvement compared with prior work.

## introduction recent progress in text summarization has been remarkable, with rouge record-setting models published every few months, and human evaluations indicating that automatically generated summaries are matching human-written summaries in terms of fluency and informativeness @xcite . a major limitation of current summarization models is their inability to remain factually consistent with the respective input document. summary inconsistencies are diverse-from inversions (i.e., negation) to incorrect use of an entity (i.e., subject, object swapping), or hallucinations (i.e., introduction of entity not in the original document). recent studies have shown that in some scenarios, even state-of-the-art pre-trained language models can generate inconsistent summaries in more than 70% of all cases @xcite . this has led to accelerated research around summary inconsistency detection. a closely related task to inconsistency detection is textual entailment, also referred to as natural language inference (nli), in which a hypothesis sentence must be classified as either entailed by, neutral, or contradicting a premise sentence. enabled by the crowd-sourcing of large nli datasets such as snli @xcite and mnli @xcite , modern architectures have achieved close to human performance at the task. the similarity of nli to inconsistency detection, as well as the availability of high-performing nli models, led to early attempts at using nli to detect consistencyerrorsinsummaries. these early attempts were unsuccessful, finding that re-ranking summaries according to an nli model can lead to an increase in consistency errors @xcite , or that out-of-the-box nli models obtain 52% accuracy at the binary classification task of inconsistency detection, only slightly above random guessing @xcite . in this work, we revisit this approach, showing that nli models can in fact successfully be used for inconsistency detection, as long as they are used at the appropriate granularity. figure 1 shows how crucial using the correct granularity as input to nli models is. an inconsistency checker should flag the last sentence in the summary (shown right) as problematic. when treating the entire document as the premise and the summary as the hypothesis, a competitive nli model predicts with probability of 0.91 that the summary is entailed by the document. however, when splitting the documents into sentence premise-hypothesis pairs (visualized as edges in figure 1 ) the nli model correctly determines that s 3 is not supported by any document sentence. this illustrates that working with sentence pairs is crucial for making nli models work for inconsistency detection. figure 1 : example document with an inconsistent summary. when running each sentence pair (d i , s j ) through an nli model, s 3 is not entailed by any document sentence. however, when running the entire (document, summary) at once, the nli model incorrectly predicts that the document highly entails the entire summary. our contributions are two-fold. first, we introduce a new approach for inconsistency detection based on the aggregation of sentence-level entailment scores for each pair of input document and summary sentences. we present two model variants that differ in the way they aggregate sentencelevel scores into a single score. summac zs performs zero-shot aggregation by combining sentence-level scores using max and mean operators. summac conv is a trained model consisting of a single learned convolution layer compiling the distribution of entailment scores of all document sentences into a single score. second, to evaluate our approach, we introduce the summac benchmark by standardizing existing datasets. because the benchmark contains the six largest summary consistency datasets, it is more comprehensive and includes a broader range of inconsistency errors than prior work. the summac models outperform existing inconsistency detection models on the benchmark, with the summac conv obtaining an overall balanced accuracy of 74.4%, 5% above prior work. we publicly release the models and datasets. 1 ## related work we briefly survey existing methods and datasets for fact checking, inconsistency detection, and inconsistency correction. ## summac models we now introduce our summac models for inconsistency detection. the first step common to all models is to apply an out-of-the-box nli model to generate an nli pair matrix for a (document, summary) pair. the two models we present then differ in the way they process this pair matrix to produce a single consistency score for a given summary. we also describe the summac evaluation benchmark, a set of inconsistency detection datasets, in section 4. in section 5, we measure the performance of the summac models on this benchmark and investigate components of the models, including which nli model achieves highest performance, which nli categories should be used, and what textual granularity is most effective. ## summac benchmark to rigorously evaluate the summac models on a diverse set of summaries with consistency judgements, we introduce a new large benchmark dataset, the summac benchmark. it comprises the six largest available datasets for summary incon-sistency detection, which we standardize to use the same classification task. ## results we compared the summac models against a wide array of baselines and state-of-the-art methods. ## conclusion we introduce summac zs and summac conv , two nli-based models for summary inconsistency detection based on the key insight that nli models require sentence-level input to work best. both models achieve strong performance on the sum-mac benchmark, a new diverse and standardized collection of the six largest datasets for inconsistency detection. summac conv outperforms all prior work with a balanced accuracy score of 74.4%, an improvement of five absolute percentage points over the best baseline. to the best of our knowledge, this the first successful attempt at adapting nli models for inconsistency detection, and we believe that there are many exciting opportuni-ties for further improvements and applications of our methods.