Benchmarking the Generation of Fact Checking Explanations (2023)

Abstract: Fighting misinformation is a challenging, yet crucial, task. Despite the growing number of experts being involved in manual fact-checking, this activity is time-consuming and cannot keep up with the ever-increasing amount of fake news produced daily. Hence, automating this process is necessary to help curb misinformation. Thus far, researchers have mainly focused on claim veracity classification. In this paper, instead, we address the generation of justifications (textual explanation of why a claim is classified as either true or false) and benchmark it with novel datasets and advanced baselines. In particular, we focus on summarization approaches over unstructured knowledge (i.e., news articles) and we experiment with several extractive and abstractive strategies. We employed two datasets with different styles and structures, in order to assess the generalizability of our findings. Results show that in justification production summarization benefits from the claim information, and, in particular, that a claim-driven extractive step improves abstractive summarization performances. Finally, we show that although cross-dataset experiments suffer from performance degradation, a unique model trained on a combination of the two datasets is able to retain style information in an efficient manner.

## introduction the interaction between the modern media ecosystem and online social media has facilitated the rapid and nearly unrestricted spreading of news. while this has been a major achievement in terms of access to information, there is also an increasing need to counter the spread of misinformation, commonly conveyed through fake news. fake news is crafted with the intention to manipulate society towards a specific political, economic, or social outcome, lacking verifiable evidence and credible sources @xcite . it can represent a threat to human health and safety, e.g., by disseminating false information on disease treatment (van der linden, 2022). thus, verifying the accuracy of claims and presenting users with factual and impartial evidence to support their veracity is of utmost importance. manual fact-checking, however, is a time-consuming activity @xcite . hence, natural language processing has been suggested as an effective solution for automating this process. thus far, the main strategies have involved classifying and flagging misleading information. however, a simple classification approach can generate a backfire effect where the belief of false claims is further entrenched rather than hindered @xcite . for this reason, explaining why a claim is classified as either true or false can be a better solution. fact-checking articles could represent a valuable resource towards this end, however, on online social media platforms they are ineffective either because ordinary users are not prone to click on links to relevant resources @xcite or because these articles are excessively long to the point that users would avoid reading it @xcite . indeed, effective explanations should be simple, and only a few arguments must be provided in order to avoid an ''overkill'' backfire effect @xcite . although the work of professional factcheckers is crucial for countering misinformation @xcite , it has been shown that disproof on social media platforms is mostly carried out by ordinary users @xcite . thus, automating the explanation generation process is deemed crucial, as an aid for both fact-checkers (to increase their online activity) and for social media users (to make their intervention more effective; @xcite . still, few attempts to automatically generate explanations/justifications about claim veracity have been proposed so far @xcite . current methods for justification production include highlighting tokens with high attention weights @xcite @xcite , utilizing knowledge graphs @xcite , and modeling it as either an extractive or abstractive summarization task @xcite . in this paper, we aim at benchmarking justification production as a summarization task, by providing an exhaustive study of the performance of extractive and abstractive approaches over two novel datasets. in particular, we consider several extractive and abstractive approaches both in supervised and unsupervised settings, where we generate a justification for a given claim using a fact-checking article as a knowledge source. we also experiment with hybrid approaches combining extractive and abstractive steps in a unique pipeline. finally, we integrate the pipeline within an end-to-end claim-driven explanation generation framework. these approaches are tested both in in-domain and cross-domain configurations, by employing two different datasets. each dataset has its own style and characteristics, but they both contain claim, verdict, and article triplets (see figure 1 ). the main findings from our experiments are: (i) if an extractive approach is employed for justification production, then the sentence selection must be driven by the claim information. (ii) if no training data is available in cross-domain experiments, extractive approaches can be better than abstractive ones for justification production. (iii) high-quality justifications can be obtained by combining in a unique pipeline extractive and abstractive summarization approaches (using simple off-the-shelf language models [lms]), and by driving sentence selection and justification generation with the claim information. still, differently from previous studies, we found that the sentences extracted from the article must retain their order rather than being rearranged according to some notion of relevance. (iv) lms for abstractive summarization should be selected according to article length since there is not a one-fits-all solution: for shorter articles, 512 tokens input length lms provide better results, while using models with 1024 input length is beneficial for longer examples. (v) although cross-dataset experiments suffer from performance degradation, lm-based models are able to retain different verdict styles: fine-tuning a single lm on the union of datasets with different stylistic characteristics leads to performance similar to those obtained by fine-tuning a model for every single dataset. ## related work the process of fact-checking a news story involves determining the truthfulness of a statement (verdict prediction) and the generation of a written rationale for the verdict (justification production). the claim veracity is usually assessed through a classification task, both binary @xcite @xcite and multi-class @xcite , or through a multitask learning approach @xcite . recently, researchers are focusing on developing datasets and systems for evidence-based verdict prediction. among the most relevant datasets, notable examples include the fever dataset @xcite , scifact @xcite , covid-fact @xcite , and politihop @xcite . justification production has proven to be more challenging than verdict prediction. several approaches have been suggested, including logic-based approaches @xcite or deep-learning and attention-based techniques @xcite @xcite . nevertheless, casting justification production as a summarization task appears to be the most viable solution @xcite . thereby, explanations can be derived from manually written debunking articles either by selecting important sentences from the text (extractive approach; @xcite or by generating a new one (abstractive approach; @xcite . extractive and abstractive summarization approaches still have many problems: extractive-generated explanations cannot generate sufficiently context-full explanations, while abstractive-generated ones may lack faithfulness, given the tendency to hallucinate of these neural models @xcite . currently, the abstractive summarization technique appears to be the most viable option for generating effective justifications. nevertheless, it may not always be possible to acquire an adequate amount of training data or the necessary computational resources for highly demanding models. thus, the purpose of this paper is twofold: (i) provide sota results using simple off-the-shelf lms, and (ii) understand which is the most suitable approach for a given scenario. ## datasets for our experiments, we collected two datasets with different structural and stylistic features. the first is liar++, a derivation of liar-plus @xcite , and the second is fullfact, a completely new dataset. both datasets comprise claim, verdict, and article entries. the claim is a short text consisting of a statement that is under inspection: it can be true, partially true, or false. the verdict is usually a paragraph-long text that provides arguments to assess the truth value of the claim: in many cases, it corresponds to a debunking text. 1 finally, the article is a document that discusses the veracity of the claim using a journalistic style and contains the verifiable facts necessary to build the verdict. figure 1 illustrates an example for each element. a detailed description of the employed datasets follows. foot_1 ## experimental setup in this section, we present several experiments for the task of justification production. all the approaches can be traced back to the pipeline presented in figure 2 . given an article, we tested several extractive approaches to select relevant material. extractive summarizes were considered justifications per se or were sent to a lm pre-trained on the abstractive summarization objective. the lms, in turn, were used with or without a fine-tuning step. moreover, we selected different decoding mechanisms to drive the generation. eventually, we conduct a cross-domain experiment to evaluate the models' robustness to the style of each dataset. ## experimental results our experimental design combines all the settings described in the previous sections. extractive and abstractive approaches are concatenated in a unique pipeline tested on both l ++ and ff. additionally, we tested the generalization capabilities of the pipeline in zero-shot experiments and by integrating the two datasets into a unique model. although we tested the complete design (960 configurations, as depicted in figure 2 ), we will discuss only the most relevant findings hereafter. ## conclusions curbing misinformation with nlp tools is a crucial task. up to now, researchers have mainly l ++ →ff claim: university applications have fallen by 4%. gold verdict: the number of applicants through ucas to uk higher education institutions has gone down by 4% compared to last year. generated verdict: the total number of people applying to higher education in the uk this year (as of the final june 2017 deadline) is down 4% compared to last year, driven by falls from england, wales and the eu. there have been a number of reasons for the overall drop, including a fall in the number of 18 year olds in the uk population. we rate this claim true. @xmath0: muslim nations did not call out the people who celebrated the 9/11 attacks. gold verdict: o'reilly said that muslim nations did not ''call out'' the people who celebrated the 9/11 attacks. so far as we can tell, there was no official condemnation of people celebrating the 9/11 attacks. however, muslim governments, and religious leaders, condemned the attacks themselves, as did many average muslims. o'reilly's statement is accurate as far as it goes, but it leaves out important information that might lead someone to reach a different conclusion. that meets our definition of half true. generated verdict: there was no official condemnation from a muslim-majority nation. what we did find were many official condemnations of the attacks themselves. average muslims attended candlelight vigils and other public events to voice sympathy for the victims and to repudiate the attacks.