Venue: NAACL 2022

# Falssum: Generating Document-level NLI Examples

for Recognizing Factual Inconsistency in Summarization

Prasetya Ajie Utama\({}^{\dagger\diamond}\)  Joshua Bambrick\({}^{\dagger}\) Nafise Sadat Moosavi\({}^{\ddagger\diamond}\) Iryna Gurevych\({}^{\diamond}\)

###### Abstract

Neural abstractive summarization models are prone to generate summaries which are factually inconsistent with their source documents. Previous work has introduced the task of recognizing such factual inconsistency as a downstream application of natural language inference (NLI). However, state-of-the-art NLI models perform poorly in this context due to their inability to generalize to the target task. In this work, we show that NLI models can be effective for this task when the training data is augmented with high-quality task-oriented examples. We introduce Falsesum, a data generation pipeline leveraging a controllable text generation model to perturb human-annotated summaries, introducing varying types of factual inconsistencies. Unlike previously introduced document-level NLI datasets, our generated dataset contains examples that are diverse and inconsistent yet plausible. We show that models trained on a Falsesum-augmented NLI dataset improve the state-of-the-art performance across four benchmarks for detecting factual inconsistency in summarization.1

Footnote 1: The code to obtain the dataset is available online at [https://github.com/joshambrick/Falsesum](https://github.com/joshambrick/Falsesum)

+ Bloomberg, London, United Kingdom

\({}^{\diamond}\) UKP Lab, Technical University of Darmstadt, Germany

\({}^{\ddagger}\) Department of Computer Science, The University of Sheffield

{putama,jbambrick7}@loomberg.net

## 1 Introduction

Recent advances in conditional text generation and the availability of large-scale datasets have given rise to models which generate highly fluent abstractive summaries Lewis et al. (2019); Zhang et al. (2019). However, studies indicate that such models are susceptible to generating factually inconsistent outputs, i.e., where the content of the summary is not semantically entailed by the input document Kryscinski et al. (2019); Goodrich et al. (2019). This motivates a new line of research for recognizing factual inconsistency in generated summaries Kryscinski et al. (2020); Pagnoni et al. (2021); Wang et al. (2020); Fabbri et al. (2021).

This factual consistency problem is closely related to the task of natural language inference (NLI) whereby a **hypothesis** sentence is classified as either entailed, neutral, or contradicted by a given **premise** sentence Condoravdi et al. (2003); Dagan et al. (2006); Bowman et al. (2015). Using an input document as the premise and a corresponding generated summary as the hypothesis, earlier solutions have adopted out-of-the-box NLI models to detect factual inconsistency, albeit with limited success Falke et al. (2019); Kryscinski et al. (2020).

This poor performance largely stems from the fact that most NLI datasets are not designed to reflect the input characteristics of downstream tasks Khot et al. (2018). Such datasets may not always capture the kinds of entailment phenomena which naturally arise from neural abstractive summarization. More importantly, there is also a discrepancy in terms of the input granularity, i.e., the premises in this consistency classification task consist of multi-sentence documents while common NLI datasets use single-sentence premises.

In this work, we introduce Falsesum, a data generation pipeline that produces NLI examples consisting of documents paired with gold summaries as **positive** examples and automatically generated inconsistent summaries as **negative** examples. We propose a novel strategy to train a text generation model to render false summaries of a given document using only supervision from an existing summarization dataset Nallapati et al. (2016). In addition, our generator supports switchable input control codes to determine the type of factual error exhibited in the generated output. This design allows Falsesum to compose diverse and naturalistic outputs which more closely resemble the inconsistent summaries generated by summarization models Maynez et al. (2020). This contrasts with previous solutions (e.g., Kryscinski et al. (2020); Yin et al. (2021), which synthesize NLI examples using rule-based transformations

or language model-based replacements, limiting their diversity and ability to reflect realistic factual errors in summarization. Overall, our contributions in this paper are the following:

First, we present a novel training pipeline to create a text generation model which takes as input a pair of a document and a corresponding gold summary. It then perturbs the summary such that it is no longer factually consistent with the original document. Our strategy obviates the need for explicit examples of inconsistent summaries, using only an existing summarization dataset. We use this model to generate a large-scale NLI dataset for the task of recognizing factually inconsistent summaries. The resultant dataset consists of pairs with documents as the premise and naturalistic summaries as the hypotheses, each labeled as either **entailment** or **non-entailment**.

Second, we demonstrate the utility of our generated data for augmenting existing NLI datasets. We show that on four benchmark datasets, NLI models trained on Falsesum-augmented data outperform those trained on previous document-level NLI datasets. We conduct an analysis to show that Falsesum-generated summaries are plausible and hard to distinguish from human-written summaries. Lastly, we show that the improvement over the benchmarks is largely attributable to the diversity of factual errors that Falsesum introduces.

## 2 Related Work

This work is related to the growing body of research into factual consistency and hallucination in text generation models, particularly for summarization Cao et al. (2018). Research has found that around 30% of summaries generated by abstractive summarization models contain information which is inconsistent with the source document Kryscinski et al. (2019). This motivates the development of an automatic approach to assess factual consistency in generated summaries, in addition to the benchmark datasets to measure the progress in this task Falke et al. (2019); Kryscinski et al. (2020); Pagnoni et al. (2021); Fabbri et al. (2021).

Earlier work by Goodrich et al. (2019) proposes to use an information extraction model to extract relation tuples from the ground-truth summary text and the generated summary and then count the overlap as the measure of factuality. Eyal et al. (2019); Durmus et al. (2020); Wang et al. (2020) use a question-answering model to detect factual inconsistency by matching the predicted answers using the document and the summary as the context.

Concurrently, researchers have drawn a connection between factual consistency and natural language inference (NLI), observing that all information in a summary should be **entangled** by the source document. While this approach enables the summary to be directly evaluated without first extracting its intermediate semantic structure, earlier attempts were largely unsuccessful. Falke et al. (2019) use the probabilities assigned to the entailment label by NLI models to re-rank the summary candidates given by beam search but found no improvement in the consistency errors. Kryscinski et al. (2020) evaluate out-of-the-box NLI models on the task of inconsistency detection in a binary classification setting and show that the performance is only slightly better than majority voting.

In the same paper, Kryscinski et al. (2020) pro

Figure 1: Overview of the Falsesum generation framework. Falsesum preprocesses and formats the source document (A) and a gold summary (B) before feeding it to a fine-tuned generator model. The model produces a factually inconsistent summary, which can then be used to obtain \(\text{(A,D)}\textbf{ or }\text{(A,E)}\) as the negative (non-entailment) NLI premise-hypothesis example pair. We also use the original \(\text{(A,B)}\) as a positive NLI example (entailment).

 

pose FactCC, a synthetic NLI data generation process which applies a set of transformation rules to obtain examples of inconsistent summaries (e.g., sentence negation, entity swapping). They demonstrate that the resulting NLI model performs well on realistic test cases which are obtained by manually annotating the output of several summarization models. This highlights the importance of NLI examples beyond sentence-level granularity and which more closely resemble the input characteristics of the downstream tasks Mishra et al. (2021).2

Footnote 2: Contemporaneous work by Laban et al. (2022) attempts to improve the application of sentence-level NLI models to detect document-level factual inconsistencies using a learnable aggregation of sentence-level predictions. Our work is orthogonal since they can benefit from better quality training examples to train their aggregation weights.

While the FactCC model is moderately effective for detecting factual inconsistency, subsequent work indicates that it only performs well on easier test cases, where highly extractive summaries (i.e., those with high lexical overlap between a summary and the source document) tend to be factually consistent and more abstractive summaries are likely to be inconsistent Zhang et al. (2020). Furthermore, Goyal and Durrett (2021) show that the synthetic and rule-based nature of FactCC leads to lack of diversity of consistency error types and it poorly aligns with the error distribution found in more abstractive summaries.

Falsesum addresses these limitations using controlled natural language generation to construct an NLI dataset which better targets the summarization domain. Inspired by the recent work on controllable generation Keskar et al. (2019); Ross et al. (2021), we employ a generation model conditioned on an input code which controls the type of consistency errors induced. We further use the generated document-level NLI examples for augmentation and show that NLI models can benefit from the additional data without hurting their existing inference ability Min et al. (2020).

## 3 Falsesum Approach

### Design Overview

Falsesum takes as an input a source document D and a corresponding reference summary S\({}^{+}\). The framework then **preprocesses** and **formats** D and S\({}^{+}\) and feeds them into a generation model \(\mathcal{G}\) which outputs a factually inconsistent summary S\({}^{-}\). For each summarization example, we then have both positive **(entailment)** and negative **(non-entailment)** NLI tuples (D, S\({}^{+}\), \(Y=1\)), (D, S\({}^{-}\), \(Y=0\)), which consist of a document-level premise, a summary sentence, and the consistency label (1 indicates entailment).

Falsesum aims to produce a naturalistic S\({}^{-}\) which is contrastive with respect to its corresponding S\({}^{+}\). This means that S\({}^{+}\) and S\({}^{-}\) should be indistinguishable in their surface characteristics (e.g., style, length, vocabularies) and only differ in their factual consistency with respect to D. This ensures that the resulting NLI model learns the correct notion of factual consistency rather than discriminating based on surface features McCoy et al. (2019). In addition to naturalness, we consider the diversity of the consistency error types exhibited by S\({}^{-}\). We follow the **consistency error typology** introduced by Maynez et al. (2020), which categorizes consistency errors as either **intrinsic**, i.e., errors due to incorrect consolidation of information from the source document, or **extrinsic**, i.e., errors due to assuming _new_ information not directly inferable from the contents of the source document.

As illustrated in Figure 1, a generation model \(\mathcal{G}\) is trained to imitate the consistency mistakes of summarization models. Specifically, it generates perturbed summaries by either **(1)** incorrectly inserting pieces of information from the source document into random spans of the original summary; or **(2)** amending pieces of information in the summary by hallucinating new "facts" not present in the source document.

To this end, the framework identifies _(!i)_**what** information or "facts" in the source document are available to the generator; and _(!ii)_**where** the incorrect information can be inserted into the gold summary, which is indicated by span **masking**. We obtain both by subsequently performing **input preprocessing** and **formatting** steps (SS3.2 and SS3.3).

Next, we define the following seq2seq task to train the model \(\mathcal{G}\): "Given _(!i)_ a list of **shuffled** and **formatted** pieces of information extracted from source document and gold summary and _(!ii)_ a partially **masked** gold summary, fill in the blanks and generate the original gold summary." Note that using gold summaries means that we can apply the existing summarization corpus to train \(\mathcal{G}\) to generate more coherent and plausible sentences.

### Input Preprocessing

Following Goodrich et al. (2019), "facts" in the source document and the gold summary are de 

fined as an open information extraction (OpenIE) tuple, which represents the predicate and argument structures found in a sentence. We denote each relation tuple as \(({\tt arg}_{0},{\tt pred},\ldots,{\tt arg}_{n})\), where predicate pred describes the event (**what** happened) and its complementing semantic arguments arg represent the **who**, **to whom**, **where**, or **how** of the event. Predicates are usually the main verb of a clause. Both predicates and their arguments consist of spans of tokens Fader et al. (2011).

We use an OpenIE implementation of Pred-Patt White et al. (2016); Zhang et al. (2017), a pattern-based framework for predicate-arguments extraction.3 As illustrated in the top half of Figure 2, we extract the relation tuples from each source document and its corresponding reference summaries. To minimize the risk of \(\mathcal{G}\) inadvertently generating consistent summaries, we corrupt each extracted "fact" by removing one randomly chosen argument from each tuple. For instance, OpenIE may extract the following tuple from a sentence:

Footnote 3: We note that the quality of the OpenIE extractions may impact the overall quality of our data generation framework.

\[(\frac{{\tt Jo}}{{\tt MgO}},\underbrace{{\tt plans~{}to~{}give}}_{\text{PED}}, \underbrace{{\tt Alex}}_{\text{ARG}_{1}},\underbrace{{\tt apples}}_{\text{AR} \varnothing})\]

We then randomly choose \({\tt apples}_{\text{ARG}_{2}}\) to be removed from the tuple. We additionally lemmatize the dependency root word of each argument and predicate span, e.g., **plans to give \(\Rightarrow\) plan to give**. This forces the model to learn to correct for grammaticality by inflecting the spans when inserting them to the **masked** spans. Once all such spans are extracted and processed, they are **grouped** and **shuffled** into two lists (predicates and arguments).

### Input Formatting

Let \({\rm P=(PRED_{1},\dots,PERD_{n})}\) and \({\rm A=(ARG_{1},\ \dots, ARG_{m})}\) be the unordered lists of extracted predicates and arguments from a source document D and the summary sentence S+. Additionally, we assume a **masked** summary sentence M (described later), derived from S+, and a control code variable \(c\in\{{\tt intrinsic},{\tt extrinsic}\}\). Generator \(\mathcal{G}\) is trained to compute \(p({\sf S^{*}|P,A,M,c})\). As illustrated in the bottom half of Figure 2, we encode all the conditional variables into the following format:

* Predicates:P; Arguments:A; Code:\(c\); Summary:M In the following, we describe the key steps in the input formatting process:

Step 1: Span RemovalInitially, P and A include predicate and argument spans from the original summary which may be used to reconstruct S+. However, at **test** time we remove these "gold" spans from the two lists to force the \(\mathcal{G}\) to make consistency mistakes. The removal is also done when training the model for control code extrinsic to train \(\mathcal{G}\) to predict plausible unseen spans.4 We summarize the different input formatting in Table 1.

Footnote 4: It is possible that some spans from the source document are duplicates of gold ones. Forinstance, the document may mention “The Queen of England”, while the gold span from the summary is “The Queen”. We use a simple heuristic to remove such duplicates by searching for other spans whose (lemmatized) dependency root token is the same.

Step 2: Span ReductionTo encourage \(\mathcal{G}\) to generate fine-grained errors Pagnoni et al. (2021); Goyal and Durrett (2021), we also train it to hallucinate incorrect modifiers into spans from P and A. To this end, we randomly drop adjectives and adverbs from 10% of the gold predicate and argument spans. For instance, an argument span "recently elected prime minister" will be reduced to "minister". This teaches the model to generate the remaining part of the span given only the context provided in the formatted input.

Step 3: Control CodeTo control the type of consistency errors generated by \(\mathcal{G}\), we append the string "code:" followed by either "intrinsic" or "extrinsic" into the input tokens. The code is determined randomly with equal probability of 0.5.

Figure 2: Input format design of Falseum. The framework first extracts the predicate and argument spans from the source document and the gold summary. The spans are then corrupted, lemmatized, and shuffled before being inserted into the input template.

 

Once the code is chosen, we perform the remaining formatting steps accordingly (see Table 1).

Step 4: Summary MaskingWe derive masked summary M by replacing the spans of **randomly** selected predicates and arguments with a special token <span_i>, where \(i=0\) is reserved for the predicate, and \(i>0\) for their arguments. These tokens control **where** the incorrect information should be inserted by the generator model into the original summary (see Table 1).

### Training Falsesum

We run the Falsesum data generation pipeline on the _train_ split of the CNN/DailyMail corpus Hermann et al. (2015), originally collected for question answering, but subsequently reformulated for summarization by Nallapati et al. (2016). This dataset contains _English_ news documents paired with human-written summaries, each consisting of multiple sentences. We break the summaries down such that each Falsesum example consists of the document text and a single sentence summary. We then run the **preprocessing** and **formatting** steps on each document-summary pair. The resulting pairs of formatted input and target output are subsequently split into train and test sets which consist of 394,774 and 262,692 instances, respectively.

We use the T5-base model Raffel et al. (2020) as generator \(\mathcal{G}\) and fine-tune it on the seq2seq task described in SS3.1. The NLI examples are produced by running the fine-tuned generator on the preprocessed and formatted test split.5 This renders an equal number of positive and negative examples. In our experiments, we randomly sample 100,000 FALSEsum examples to augment the NLI dataset.

Footnote 5: See Appendix A for the hyperparameter details.

## 4 Experimental Settings

Our experiments aim to demonstrate the effectiveness of Falsesum-generated document-level examples for NLI dataset augmentation. We evaluate the downstream performance of the NLI models by testing them against several benchmarks for determining the factual inconsistency of generated summaries. In this section, we describe the training setup of the NLI models, including the model and both the sentence- and document-level datasets.

### Training

NLI modelsWe train several NLI models by fine-tuning RoBERTa-baseLiu et al. (2019) on _either_ the original or the augmented MNLI dataset Williams et al. (2018). The MNLI dataset consists of 392,702 train instances, each labeled

\begin{table}
\begin{tabular}{l|p{142.3pt}|p{142.3pt}|p{142.3pt}} \hline \hline
**Mode** & **Input** & **Expected Output** & **Description** \\ \hline \multirow{3}{*}{**train** intrinsic} & Predicates: caught, plead guilty to, appear before, face: Arguments: the corruption scandal, Two Pennsylvania judges & Model learns to combines listed spans to produce most plausible summary. \\  & **nia judges:** many children, the U.S: Code: intrinsic; Summary: semantic segment/de federal fraud charges; & & \\ \hline \multirow{3}{*}{**test** intrinsic} & Predicates: caught, plead guilty to, appear before, face: Arguments: the corruption scandal, Two Pennsylvania judges & Many of the children face federal fraud charges. & Model consolidates incorrect information. \\  & **nia judges:** many children, the U.S: Code: intrinsic; Summary: semantic segment/de federal fraud charges. & & \\ \hline \hline \multirow{3}{*}{**train** extrinsic extrinsic} & Predicates: in-presence for limit, face, is being erode, is fight, Arguments: panelist action, item, sea level Arctic melt, atthe-climate change conference. Code: extrinsic: Summary: The Alliance Signature: Semantic Segment/de. & The Alliance is pressing for action at the climate change conference. & Model learns to hallucinate new unsupported information. \\ \hline \multirow{3}{*}{**test** extrinsic extrinsic} & Predicates: in-presence for limit, face, is being erode, is fight: Arguments: panelist action, item, sea level Arctic melt, atthe-climatechange conference. Code: extrinsic: Summary: The Alliance Signature: Semantic Segment/de. & The Alliance is planning to impose limits on emissions. & Model hallucinates new unsupported information. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Examples of input formatting on two different summarization instances for both intrinsic and extrinsic error types during training and testing. Gold input spans (indicated by **boldface**), which are extracted from the gold summary, are only visible to the model during intrinsic training. They are removed from the input in all other settings, as indicated by strikethrough text.

 

as either "_entailment_", "_neutral_", or "_contradiction_". To enable the application of NLI data to this factual consistency task, we use a binary formulation of NLI, where the "_neutral_" and "_contradiction_" labels are combined into "_non-entailment_". The document-level inputs are formatted similarly to sentence-level examples, i.e., the document premise D and summary hypothesis (S\({}^{+}\) or S\({}^{-}\)) are concatenated and a special classification token ([CLS]) is used Devlin et al. (2019).

Document-level NLI datasetsWe conduct augmentation comparisons with several multi-sentence NLI datasets which obtain examples from _news_ or _summarization_ domains. We consider the following datasets: **ANLI**Nie et al. (2020), a paragraph-level NLI dataset collected via an iterative and adversarial human-in-the-loop annotation protocol. It consists of mostly Wiki data but also includes a small portion of news text; **DocNLI**Yin et al. (2021), a document-level NLI dataset containing multi-sentence premise and hypothesis sentences, collected by converting QA examples to NLI instances Demszky et al. (2018) and replacing words and sentences in _news_ summaries using a language model; **FactCC**Kryscinski et al. (2020), a large-scale dataset specifically generated for training summary factual correctness classification models. The positive examples in FactCC are obtained by backtranslating a random sentence from a CNN/DailyMail _news_ story, while negative examples are obtained by perturbing the sentence using predefined rules, e.g., entity swapping. For fair comparison, we sample 100,000 examples from each augmentation dataset in our experiments.

### Benchmark Datasets

We evaluate these NLI models on four benchmark datasets to classify the factual consistency of abstractive summaries. These datasets differ in terms of the annotation protocol, the granularity of the summaries (single- or multi-sentence), the summarization corpus used, and the models used to generate the summaries that are annotated. The tasks are formulated as a binary classification with the labels "_consistent_" and "_inconsistent_". We evaluate NLI models on these tasks by mapping the predicted label "_entailment_" to "_consistent_" and "_non-entailment_" to "_inconsistent_". The benchmarks datasets are detailed in the following:

FactCCIn addition introducing a synthetic training dataset for the task, Kryscenski et al. (2020) introduce a manually annotated test set. It contains 1,431 document and single-sentence summary pairs generated by various neural abstractive summarization models trained on CNN/DailyMail corpus.6

Footnote 6: We merge the test and validation sets into a single test set.

Ranksum Falke et al. (2019) formulate the factual consistency problem in summarization as a ranking task. They introduce a dataset consisting of 107 documents, each paired with a set of five ranked summary candidates obtained from the beam search of a summarization model. Given the manually annotated consistency label on summary candidates, the task is to re-rank the list such that the top-1 summary is factually consistent.

Summeval Fabbri et al. (2021) introduce a comprehensive benchmark for factual consistency detection in summarization. It includes summaries generated by seven extractive models and sixteen abstractive models, which are judged by three annotators using a 5-point Likert scale.7

Footnote 7: We aggregate the label as “consistent” if all annotators rated the summary as a 5 and “inconsistent” otherwise.

QAGsThe dataset collected by Wang et al. (2020) consists of 239 test set instances from XSUM Narayan et al. (2018) and 714 instances from CNN/DailyMail.8 Each instance consists of a pair of a source document and a single-sentence summary, which is labeled via majority voting on three annotators' labels.

Footnote 8: This is the number of instances after we split multi-sentence summaries into separate single-sentence summary test instances, where an individual factuality judgement is available.

## 5 Results and Discussion

### Main Results

Performance on FactCC, QAGS, and SummEval is measured using balanced accuracy, which is suitable for class imbalanced settings, since the factually consistent label is the majority in some benchmark datasets. It is defined as the average recall of the two classes, such that majority label voting obtains only a 50% score. To measure ranking performance in Ranksum, we calculate the average Precision@1, which computes the fraction of times a factually consistent summary is ranked highest on each test instance. We perform five training runs for each setup using different random seeds and take the mean to address performance instability Reimers and Gurevych (2017).

 

From the results in Table 2, we observe the following: **(1)** Models trained on sentence-level MNLI datasets perform poorly when evaluated directly on document-level benchmarks, even after we increase the maximum input token length from 128 to 512;9 **(2)** This limitation can be alleviated by the sentence-wise prediction strategy ([split-doc]MNLI-128),10 which achieves 66.63. Note, however, that this improvement comes at the expense of compute cost which is multiplied by a significant factor; **(3)** DocNLI and ANLI perform poorly even though they contain longer premise sentences, indicating that the length mismatch may not be the primary issue; **(4)** Falsesum obtains substantial improvement over the previous state-of-the-art FactCC, despite being derived from the same summarization dataset (CNN/DailyMail). This indicates that Falssum provides higher quality examples and includes more types of entailment phenomena that occur naturally in this task.

Footnote 9: Average context word count is only 22 in MNLI and 546 in FactCC.

### Ablation Analysis on Falsesum Data

We perform an ablation analysis to study how each component of our data generation pipeline contributes to the final performance. We first remove the contrastive property of the Falsesum data by randomly including only **either** the positive \((\text{D},\text{S}^{+},Y=1)\)**or** negative \((\text{D},\text{S}^{-},Y=0)\) NLI examples obtained from a single \((\text{D},\text{S}^{+})\) pair. Next, we filter out the negative NLI instances that are generated using either intrinsic or extrinsic code. We refer to the three ablated datasets as -contrastive, -intrinsic and -extrinsic, respectively. We set the sampled training size to 100,000 for the three ablation setups and aggregate the results from five training runs.

Table 3 shows the performance of the ablated models. We observe that removing contrastive pairs in the augmented training data results in a 1.06% drop on the overall benchmarks score. We also see that removing intrinsic error examples results in the highest performance loss, \(-5.03\%\) compared to \(-2.22\%\) by -extrinsic. This is explained by the fact that intrinsic consistency errors are more dominant on benchmarks that are built on the CNN/DailyMail corpus Goyal and Durrett (2021). We conclude that all the above properties are important for the overall improvements obtained by Falsesum.

### Fine-grained Evaluation

Previous work has shown that NLI models are prone to relying on fallible heuristics which associate lexical overlap with entailment labels McCoy et al. (2019). In the factual consistency task, this corresponds to models associating highly extractive summaries with the "consistent" label. This raises a question about whether Falsesum data alleviates this tendency in the resulting NLI models.

To answer this question, we partition the FactCC annotated test examples into five ordered subsets based on the lexical overlap between their

\begin{table}
\begin{tabular}{c|l|c c c c|c} \hline \hline  & & \multicolumn{7}{c|}{Benchmark Datasets} \\
* & 50.00 & 50.46 & 50.00 & 50.00 & 50.11 \\ \hline
* & 57.39 & 57.01 & 59.72 & 54.11 & 57.06 \\
* & 72.07 & 68.03 & 71.08 & 55.32 & 66.63 \\ \hline
* & 57.93 & 51.40 & 52.73 & 48.75 & 51.43 \\
**MNLI**-512 & **ANLI** & 53.91 & 55.76 & 53.54 & 49.56 & 53.19 \\
**MNLI**-512 & **DocILI** & 58.13 & 53.58 & 57.10 & 52.59 & 55.35 \\
**MNLI**-512 & **FactCC** & 73.87 & 67.29 & 73.50 & 60.04 & 69.02 \\
**MNLI**-512 & **FalseNum** (ours) & **83.52** & **72.90** & **75.05** & **65.18** & **74.17** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance of MNLI models with different augmentation data across benchmarks to classify the factual consistency of summaries. **MNLI**-128 and **MNLI**-512 are RoBERTa–base models trained using maximum token length of 128 and 512, respectively.

\begin{table}
\begin{tabular}{l|c c} \hline \hline
**Training Dataset** & **Overall** & \(\Delta\) \\ \hline MNLI+Falsesum & **74.17** & \\ MNLI+Falsesum -Contrastive & 73.11 & -1.06 \\ MNLI+Falsesum -Extrinsic & 71.95 & -2.22 \\ MNLI+Falsesum -Intrinsic & 69.14 & **-5.03** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Model performance when trained on ablated Falsesum dataset. Excluding the contrastive, extrinsic, and intrinsic examples results in lower overall performance, indicating each property is beneficial.

 

summary hypothesis and the source document premise. We define an overlap score using the normalized coverage and density summary extractiveness scores introduced by Grusky et al. (2018). Both measures have the range \([0.0,1.0]\), where \({\tt density}=1.0\) indicates that all words in a summary are also present in the source document and normalized coverage \(=1.0\) indicates that the summary is obtained by copying a continuous fragment of the source document. We then define overlap = normalized coverage \(\times\) density.

Figure 3 shows the comparison of FactCC and Falsesum augmentation performance across varying lexical overlap scores. We see that Falsesum performs better on all subsets of the FactCC test set with the greatest performance gap appearing on the 0.9 overlap subset. Upon closer inspection, we see that the FactCC model makes mostly false positive classification errors on this subset, i.e., it tends to predict highly extractive summaries as "consistent", leading to near majority voting performance of 50%. Falsesum, on the other hand, better discriminates the factual consistency of examples without over-relying on lexical overlap.

### Data Quality Analysis

We conduct both manual and automatic quality evaluation of the Falsesum-generated dataset. First, we sample 200 generated negative examples and manually verify whether (i) the perturbed summary S\({}^{-}\) is indeed factually inconsistent; (ii) the type of consistency error follows the specified control code; (iii) the incorrect "fact" is inserted at the specified missing span. Following Kryscinski et al. (2020), the authors perform this annotation to avoid high disagreement by crowd annotators in this task Falke et al. (2019). The results in Table 4 show that about 86% of intrinsic 81% of extrinsic generated error examples are factually inconsistent, which happen due to several reasons, e.g., generator model chooses a span from the list that is similar to the original span, or generator model correctly guesses the original missing span. This further suggests that pre-trained language models such as RoBERTa-base can be robust against the induced label noise and can still learn a performant classifier. While \(\mathcal{G}\) almost always inserts the incorrect "fact" at the specified positions, we observe that it often fails to follow the specified extrinsic code correctly. We suspect that this is because the model prefers the easier task of copying the input over generating novel phrases.11

Footnote 11: We include more examples of generated NLI instances as well as the inadvertently consistent output in Appendix D.

Following Gururangan et al. (2018), we also evaluate the naturalness of the generated dataset. We train an NLI model using positive examples from CNN/DailyMail and Falsesum-generated negative examples. The model receives no premise so must distinguish between entailed and non-entailed hypotheses using semantic plausibility or spurious surface features, e.g., grammatical mistakes or fluency errors. The relatively low accuracy of these models on Falsesum data (shown in Table 5) suggests that, compared to FactCC and DocNLI, Falsesum-generated summaries are relatively hard to distingu

\begin{table}
\begin{tabular}{l|p{71.1pt}|p{71.1pt}|p{71.1pt}} \hline \hline
**Code** & **Label ✓** & **Type ✓** & **Span ✓** \\ \hline Intrinsic & 86\% & 94\% & 94\% \\ Extrinsic & 81\% & 65\% & 95\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Manual verification of Falsesum-generated NLI examples. Label, type, and span indicate the percentage of generated summaries with correct inconsistency label, error type, and error span, respectively.

Figure 3: Comparison between NLI models augmented with Falsesum and FactCC across different measures of summary extractiveness. The x-axis shows the median overlap score of each test subset.

 



## Conclusion

NLI models present a promising solution for automatic assessment of factual consistency in summarization. However, the application of existing models for this task is hindered by several challenges, such as the mismatch of characteristics between their training dataset and the target task data. This mismatch includes the difference in terms of the input granularity (sentence vs. document level premises) and the types of (non-)entailment phenomena that must be recognized.

In this work, we present Falsesum, a data generation pipeline which renders large-scale document-level NLI datasets without manual annotation. Using our training strategy, we demonstrate that it is possible to learn to generate diverse and naturalistic factually inconsistent (non-entailed) summaries using only existing (entangled) consistent summaries for training. We show that the resultant data is effective for augmenting NLI datasets to improve the state-of-the-art performance across four summary factual inconsistency benchmarks.

## Acknowledgments

We would like to thank Marco Ponza, Marco Fiscato, Umut Topkara and other colleagues from Bloomberg AI for the thoughtful discussion and feedback throughout this project. We also thank Leonardo Ribeiro for comments on the earlier version of this work and the anonymous reviewers for their constructive feedback. The authors affiliated with UKP were supported by the German Research Foundation through the research training group "Adaptive Preparation of Information from Heterogeneous Sources" (AIPHES, GRK 1994/1) and by the German Federal Ministry of Education and Research and the Hessian State Ministry for Higher Education, Research and the Arts within their joint support of the National Research Center for Applied Cybersecurity ATHENE.

## References

* Bowman et al. (2015) Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015. A large annotated corpus for learning natural language inference. In _Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing_, pages 632-642, Lisbon, Portugal. Association for Computational Linguistics.
* Cao et al. (2018) Ziqiang Cao, Furu Wei, Wenjie Li, and Sujian Li. 2018. Faithful to the original: Fact aware neural abstractive summarization. In _Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18), the 30th innovative Applications of Artificial Intelligence (IAAI-18), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18), New Orleans, Louisiana, USA, February 2-7, 2018_, pages 4784-4791. AAAI Press.
* Condoravdi et al. (2003) Cleo Condoravdi, Dick Crouch, Valeria de Paiva, Reinhard Stolle, and Daniel G. Bobrow. 2003. Entailment, intensionality and text understanding. In _Proceedings of the HLT-NAACL 2003 Workshop on Text Meaning_, pages 38-45.
* Dagan et al. (2006) Ido Dagan, Oren Glickman, and Bernardo Magnini. 2006. The pascal recognising textual entailment challenge. In _Machine Learning Challenges. Evaluating Predictive Uncertainty, Visual Object Classification, and Recognising Technical Entailment_, pages 177-190, Berlin, Heidelberg. Springer Berlin Heidelberg.
* Demszky et al. (2018) Dorottya Demszky, Kelvin Guu, and Percy Liang. 2018. Transforming question answering datasets into natural language inference datasets. _CoRR_, abs/1809.02922.
* Devlin et al. (2019) Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In _Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)_, pages 4171-4186, Minneapolis, Minnesota. Association for Computational Linguistics.
* Durmus et al. (2020) Esin Durmus, He He, and Mona Diab. 2020. FEQA: A question answering evaluation framework for faithfulness assessment in abstractive summarization. In _Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics_, pages 5055-5070, Online. Association for Computational Linguistics.
* Eyal et al. (2019) Matan Eyal, Tal Baumel, and Michael Elhadad. 2019. Question answering as an automatic evaluation metric for news article summarization. In _Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)_, pages 3938-3948, Minneapolis, Minnesota. Association for Computational Linguistics.
* Fabbri et al. (2021) Alexander R. Fabbri, Wojciech Kryscinski, Bryan McCann, Caiming Xiong, Richard Socher, and Dragomir Radev. 2021. SummEval: Re-evaluating Summarization Evaluation. _Transactions of the Association for Computational Linguistics_, 9:391-409.
* Fader et al. (2011) Anthony Fader, Stephen Soderland, and Oren Etzioni. 2011. Identifying relations for open information extraction. In _Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing_

pages 1535-1545, Edinburgh, Scotland, UK. Association for Computational Linguistics.
* Falke et al. (2019) Tobias Falke, Leonardo F. R. Ribeiro, Prasetya Ajie Utama, Ido Dagan, and Iryna Gurevych. 2019. Ranking generated summaries by correctness: An interesting but challenging application for natural language inference. In _Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics_, pages 2214-2220, Florence, Italy. Association for Computational Linguistics.
* Goodrich et al. (2019) Ben Goodrich, Vinay Rao, Peter J. Liu, and Mohammad Saleh. 2019. Assessing the factual accuracy of generated text. In _Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining_, KDD '19, page 166-175, New York, NY, USA. Association for Computing Machinery.
* Ooyal and Durrett (2021) Tanya Ooyal and Greg Durrett. 2021. Annotating and modeling fine-grained factuality in summarization. In _Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies_, pages 1449-1462, Online. Association for Computational Linguistics.
* Grusky et al. (2018) Max Grusky, Mor Naaman, and Yoav Artzi. 2018. Newsroom: A dataset of 1.3 million summaries with diverse extractive strategies. In _Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)_, pages 708-719, New Orleans, Louisiana. Association for Computational Linguistics.
* Gururangan et al. (2018) Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel Bowman, and Noah A. Smith. 2018. Annotation artifacts in natural language inference data. In _Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)_, pages 107-112, New Orleans, Louisiana. Association for Computational Linguistics.
* Hermann et al. (2015) Karl Moritz Hermann, Tomas Kocisky, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, and Phil Blunsom. 2015. Teaching machines to read and comprehend. In _Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, December 7-12, 2015, Montreal, Quebec, Canada_, pages 1693-1701.
* Keskar et al. (2019) Nitish Shirish Keskar, Bryan McCann, Lav R. Varshney, Caiming Xiong, and Richard Socher. 2019. CTRL: A conditional transformer language model for controllable generation. _CoRR_, abs/1909.05858.
* Khot et al. (2018) Tushar Khot, Ashish Sabharwal, and Peter Clark. 2018. Scitail: A textual entailment dataset from science question answering. In _Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), the 30th innovative Applications of Artificial Intelligence (IAAI-18), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18), New Orleans, Louisiana, USA, February 2-7, 2018_, pages 5189-5197. AAAI Press.
* Kryscinski et al. (2019) Wojciech Kryscinski, Nitish Shirish Keskar, Bryan McCann, Caiming Xiong, and Richard Socher. 2019. Neutral text summarization: A critical evaluation. In _Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)_, pages 540-551, Hong Kong, China. Association for Computational Linguistics.
* Kryscinski et al. (2020) Wojciech Kryscinski, Bryan McCann, Caiming Xiong, and Richard Socher. 2020. Evaluating the factual consistency of abstractive text summarization. In _Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)_, pages 9332-9346, Online. Association for Computational Linguistics.
* Laban et al. (2022) Philippe Laban, Tobias Schnabel, Paul N. Bennett, and Marti A. Hearst. 2022. SummaC: Re-Visiting NLIR-based Models for Inconsistency Detection in Summarization. _Transactions of the Association for Computational Linguistics_, 10:163-177.
* Lewis et al. (2019) Mike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad, Abdelrahman Mohamed, Omer Levy, Veselin Stoyanov, and Luke Zettlemoyer. 2019. BART: denoising sequence-to-sequence pretraining for natural language generation, translation, and comprehension. _CoRR_, abs/1910.13461.
* Liu et al. (2019) Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A robustly optimized BERT pretraining approach. _CoRR_, abs/1907.11692.
* Maynez et al. (2020) Joshua Maynez, Shashi Narayan, Bernd Bohnet, and Ryan McDonald. 2020. On faithfulness and factuality in abstractive summarization. In _Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics_, pages 1906-1919, Online. Association for Computational Linguistics.
* McCoy et al. (2019) Tom McCoy, Ellie Pavlick, and Tal Linzen. 2019. Right for the wrong reasons: Diagnosing syntactic heuristics in natural language inference. In _Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics_, pages 3428-3448, Florence, Italy. Association for Computational Linguistics.
* Min et al. (2020) Junghyun Min, R. Thomas McCoy, Dipanjan Das, Emily Pitler, and Tal Linzen. 2020. Syntactic data augmentation increases robustness to inference heuristics. In _Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics_, pages 2339-2352, Online. Association for Computational Linguistics 

* Mishra et al. (2021) Anshuman Mishra, Dhruvesh Patel, Aparna Vijayakumar, Xiang Lorraine Li, Pavan Kapanipathi, and Kartik Talamadupula. 2021. Looking beyond sentence-level natural language inference for question answering and text summarization. In _Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies_, pages 1322-1336, Online. Association for Computational Linguistics.
* Nallapati et al. (2016) Ramesh Nallapati, Bowen Zhou, Cicero dos Santos, Caglar Gucehre, and Bing Xiang. 2016. Abstractive text summarization using sequence-to-sequence RNNs and beyond. In _Proceedings of The 20th SIGNLL Conference on Computational Natural Language Learning_, pages 280-290, Berlin, Germany. Association for Computational Linguistics.
* Narayan et al. (2018) Shashi Narayan, Shay B. Cohen, and Mirella Lapata. 2018. Don't give me the details, just the summary! topic-aware convolutional neural networks for extreme summarization. In _Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing_, pages 1797-1807, Brussels, Belgium. Association for Computational Linguistics.
* Nie et al. (2020) Yuyang Nie, Yuanhe Tian, Yan Song, Xiang Ao, and Xiang Wan. 2020. Improving named entity recognition with attentive ensemble of syntactic information. In _Findings of the Association for Computational Linguistics: EMNLP 2020_, pages 4231-4245, Online. Association for Computational Linguistics.
* Pagnoni et al. (2021) Artidoro Pagnoni, Vidhisha Balachandran, and Yulia Tsvetkov. 2021. Understanding factuality in abstractive summarization with FRANK: A benchmark for factuality metrics. In _Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies_, pages 4812-4829, Online. Association for Computational Linguistics.
* Raffel et al. (2020) Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020. Exploring the limits of transfer learning with a unified text-to-text transformer. _J. Mach. Learn. Res._, 21:140:1-140:67.
* Reimers and Gurevych (2017) Nils Reimers and Iryna Gurevych. 2017. Reporting score distributions makes a difference: Performance study of LSTM-networks for sequence tagging. In _Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing_, pages 338-348, Copenhagen, Denmark. Association for Computational Linguistics.
* Ross et al. (2021) Alexis Ross, Tongshuang Wu, Hao Peng, Matthew E. Peters, and Matt Gardner. 2021. Tailor: Generating and perturbing text with semantic controls. _CoRR_, abs/2107.07150.
* Wang et al. (2020) Alex Wang, Kyunghyun Cho, and Mike Lewis. 2020. Asking and answering questions to evaluate the factual consistency of summaries. In _Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics_, pages 5008-5020, Online. Association for Computational Linguistics.
* White et al. (2016) Aaron Steven White, Drew Reisinger, Keisuke Sakaguchi, Tim Vieira, Sheng Zhang, Rachel Rudinger, Kyle Rawlins, and Benjamin Van Durme. 2016. Universal decompositional semantics on Universal Dependencies. In _Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing_, pages 1713-1723, Austin, Texas. Association for Computational Linguistics.
* Williams et al. (2018) Adina Williams, Nikita Nangia, and Samuel Bowman. 2018. A broad-coverage challenge corpus for sentence understanding through inference. In _Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)_, pages 1112-1122, New Orleans, Louisiana. Association for Computational Linguistics.
* Yin et al. (2021) Wenpeng Yin, Dragomir Radev, and Caiming Xiong. 2021. DocNLI: A large-scale dataset for document-level natural language inference. In _Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021_, pages 4913-4922, Online. Association for Computational Linguistics.
* Zhang et al. (2019) Jingqing Zhang, Yao Zhao, Mohammad Saleh, and Peter J. Liu. 2019. PEGASUS: pre-training with extracted gap-sentences for abstractive summarization. _CoRR_, abs/1912.08777.
* Zhang et al. (2017) Sheng Zhang, Rachel Rudinger, and Benjamin Van Durme. 2017. An evaluation of PredPatt and open IE via stage 1 semantic role labeling. In _IWCS 2017 -- 12th International Conference on Computational Semantics -- Short papers_.
* Zhang et al. (2020) Yuhui Zhang, Yuhao Zhang, and Christopher D. Manning. 2020. A close examination of factual correctness evaluation in abstractive summarization 

Hyperparameters

Generator modelWe train a T5-base model for three epochs with batch size of 24 using the AdamW optimizer. We set the maximum source token length to 256 and the target token length to 42. We use a learning rate of \(3e^{-5}\) and fix the random seed to 11. For decoding, we set the minimum and maximum sequence length to 10 and 60, respectively. We sample using beam search with a beam of size two. We additionally set the repetition penalty to 2.5 and the length penalty to 1.0.

Classification modelWe train RoBERTa-base models on augmented and original MNLI datasets for three epochs with a batch size of 32. The learning rate is set to \(1e^{-5}\), while the maximum input token length is set to either 128 or 512. We use the following random seeds for the five training runs: 11, 12, 13, 14, and 15.

## Appendix B Aggregating Predictions

We follow Falke et al. (2019) to adapt out-of-the-box MNLI models to document-level input by performing a sentence-wise prediction before aggregating the output. Given a document D consisting of sentences \(d_{1},\dots,d_{n}\), and a multi-sentence summary \(S\) consisting of \(s_{1},\dots,s_{m}\), we aggregate the probability scores given by the classifier model \(F\) on each \(d_{i},s_{j}\) pair. The aggregated consistency score \(\sigma(\text{D},S)\) is given by:

\[\sigma(\text{D},S)=\frac{1}{m}\sum_{j=1}^{m}\max_{d\in\text{D}}F(d,s_{j})\]

This means that it is sufficient for a summary sentence to be factually consistent given only a single entailing sentence in the source document. We then take the average scores across the summary sentences since each of them needs to be entailed by the source document. We use a similar aggregation method to evaluate augmented MNLI models on multi-sentence summaries from the Summeval and Ranksum benchmarks.

## Appendix C Falesum Details

In the preprocessing steps, we only perform the predicate and argument span extraction on the first 15 sentences for computational efficiency. For training, this is not an issue since the gold spans from the reference summary are included in the input. Additionally, we may extract multiple OpenIE relation tuples from each sentence. To avoid having overlapping spans from a single input, we randomly select two tuples from each sentence.

## Appendix D Falsesum Examples

We include more examples of generated NLI instances in Table 6. We also include cases where Falsesum inadvertently generates factually consistent summaries in Table 7. Lastly, we show several examples of the formatted input and the generated output at **test** time in Table 8.

 

Mexican federal police have arrested a fugitive on the FBI's 10 Most Wanted list, Mexican authorities said. Jorge Alberto Lopez Orozco allegedly murdered his girlfriend and her two young sons. Jorge Alberto Lopez Orozco is wanted in Elmore County, Idaho, on charges that he shot and killed three people, the FBI said. The charred remains of a woman and her sons, ages 2 and 4, were found inside a burned-out vehicle on August 11, 2002, it said. Each victim had been shot in the head or chest. The FBI was still working Friday to confirm the identity of the man in custody, said Debbie Dujanovic, a spokeswoman in the agency's Salt Lake City, Utah, field office. The Salt Lake City office has jurisdiction in the case.

\begin{table}
\begin{tabular}{c l} \hline \multicolumn{1}{l}{} & \multicolumn{1}{l}{} \\  & entailment & She died in 2000 of multiple sclerosis and funds raised will go to charity. \\ (extrinsic) non-entailment & She died in 2000 of multiple sclerosis and every penny she saves will go to charity. \\ \end{tabular}
\end{table}
Table 6: Examples of NLI pairs generated by Falsesum. We show both the entailment and non-entailment hypotheses obtained from each source document. Green-highlighted spans indicate the information used consistently in the summary. Red-highlighted spans indicate information used or inserted by the model to generate an inconsistent summary.

 

The Mojito, a Cuban mix of white rum, sugar, lime, mint and soda water, is the most popular cocktail in Britain according to a report. Sales of cocktails have risen by more than 10 per cent in the past two years. More than one in five of Britain's pubs and bars now serve cocktails and the Mojito - a Cuban mix of white rum, sugar, lime, mint and soda water - is the most popular, according to a report. Pina Coladas (rum, coconut and pineople juice) and Woo Woos (vodka, peach schnendaps and cranberry juice) were also popular. The Mixed Drinks Report, by consultancy firm CGA Strategy, found more women than men choose cocktails, as 54 per cent of cocktail drinkers are female. Bomb and pitcher serves remain popular, with 74 per cent of 18 to 24-year-olds admitting to have bought a bomb drink, while nine in 10 in the same age range say they drink pitchers. Cocktails are enjoyed by the core 18 to 35-year-old demographic 'in all on-trade occasions' including throughout the night, as opposed to just the start.

\begin{table}
\begin{tabular}{l l} \hline \hline  & gold & Park Service is dealing with a S9 billion backlog of maintenance needs. \\ (intrinsic) generated & America’s 391 national parks are facing a S9 billion backlog of maintenance needs. \\ \hline \hline \end{tabular}
\end{table}
Table 7: Falsesum-generated summaries that are unintentionally consistent with the source document. Green-highlightlighted spans indicate information which is consistent with the document.

\begin{table}
\begin{tabular}{c c} \hline \hline
**Predicates** : is being offer for; were steal from, sell; Both as a solo artist and leader of the Heartbreakers, is one of ! \\ \hline according to, where were rehearse for; contribute to, was induct into in: & Arguments : the Heartbreakers, The band; Denise \\ Quan, five guitars, the Recording Industry Association of America, more than 57 million albums, Petty, A 7,500 reward, a \\ soundstage, the Rock \& Roll Hall of Fame: [Code : intrinsic: Summary : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Expant : Exp : Expant : Expant : Expant : Exp : Expant : Expant : Expant : Exp : Expant : Expant : Exp : Expant : Expant : Exp : Expant : Expant : Exp : Expant : Expant : Exp : Expant : Expant : Exp : Expant : Expant : Exp : Expant : Expant : Exp : Expant : Expant : Exp