| Challenge: | Automating fact-checking is a time-consuming task that cannot keep up with the ever-increasing amount of fake news produced daily. |
| Approach: | They propose to automate the process of fact-checking by generating justifications from textual explanations of why a claim is classified as either true or false. |
| Outcome: | The proposed approach improves summarization performance over unstructured knowledge and with two datasets with different styles and structures. |
Similar Papers
Generating Fact Checking Explanations (2020.acl-main)
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| Challenge: | Existing work on automated fact checking is concerned with predicting the veracity of claims based on metadata, social network spread, language used in claims, and, more recently, evidence supporting or denying claims. |
| Approach: | They propose to combine the generation of justifications for verdicts on claims with the multi-task model to optimize both objectives at the same time rather than training them separately. |
| Outcome: | The proposed model improves the informativeness, coverage and overall quality of the generated explanations, rather than training them separately. |
Harnessing Abstractive Summarization for Fact-Checked Claim Detection (2022.coling-1)
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| Challenge: | Social media platforms are becoming battlegrounds for anti-social elements . fact-checking organizations cannot cope with the rapid dissemination of misinformation . a new workflow for fact- checking can be implemented to reduce human time for tasks with high cognition . |
| Approach: | They propose a workflow for detecting previously fact-checked claims that uses abstractive summarization to generate crisp queries. |
| Outcome: | The proposed workflow achieves Recall@5 and MRR of 35% and 0.3, respectively. |
Explainable Automated Fact-Checking for Public Health Claims (2020.emnlp-main)
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| Challenge: | a few blind spots exist in the state-of-the-art in fact-checking for political claims. |
| Approach: | They propose to use a dataset of 11.8K claims to explain fact-check labels for claims . they define and evaluate three coherence properties of explanation quality with humans . |
| Outcome: | The proposed model can be trained on in-domain data and evaluates its coherence properties with humans and computationally. |
Evaluating the Factual Consistency of Abstractive Text Summarization (2020.emnlp-main)
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| Challenge: | a weakly-supervised approach is needed to verify factual consistency . auxiliary span extraction tasks are useful for verifying factual consistent summaries . |
| Approach: | They propose a weakly-supervised approach for verifying factual consistency . they transfer the model to summaries generated by several neural models . |
| Outcome: | The proposed approach outperforms models trained with strong supervision on source documents and human evaluations. |
Automated Justification Production for Claim Veracity in Fact Checking: A Survey on Architectures and Approaches (2024.acl-long)
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| Challenge: | Current research focuses on predicting claim veracity through metadata analysis and language scrutiny, with an emphasis on justifying verdicts. |
| Approach: | They propose a comprehensive taxonomy for categorizing works based on various criteria and propose scalable methodologies for improving fact-checking explainability. |
| Outcome: | The proposed taxonomy identifies challenges while proposing future directions in fact-checking explainability. |
NewsClaims: A New Benchmark for Claim Detection from News with Attribute Knowledge (2022.emnlp-main)
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Revanth Gangi Reddy, Sai Chetan Chinthakindi, Zhenhailong Wang, Yi Fung, Kathryn Conger, Ahmed ELsayed, Martha Palmer, Preslav Nakov, Eduard Hovy, Kevin Small, Heng Ji
| Challenge: | Current claims detection methods focus on sentence analysis, ignoring other attributes . a key element of identifying misinformation is detecting the claims and the arguments that have been presented. |
| Approach: | They propose a benchmark for attribute-aware claim detection in the news domain . they extend the problem to include extraction of additional attributes related to each claim . |
| Outcome: | The proposed system performs well on the test, but human performance is still poor. |
Understanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality Metrics (2021.naacl-main)
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| Challenge: | Modern summarization models generate fluent but often factually unreliable outputs. |
| Approach: | They propose to use human annotations to identify different categories of factual errors and benchmark factuality metrics to improve summarization evaluation. |
| Outcome: | The proposed method identifies the proportion of different categories of factual errors and benchmarks their human judgements as well as their specific strengths and weaknesses. |
SummaCoz: A Dataset for Improving the Interpretability of Factual Consistency Detection for Summarization (2024.findings-emnlp)
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| Challenge: | Summarization is an important application of Large Language Models. |
| Approach: | They integrate human-annotated and model-generated natural language explanations to elucidate how a summary deviates and becomes inconsistent with its source article. |
| Outcome: | The proposed model provides rationales for its judgments and improves its accuracy significantly. |
Explainable Automated Fact-Checking: A Survey (2020.coling-main)
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| Challenge: | Steady progress has been made in fact-checking and its orthogonal tasks. |
| Approach: | They propose to use fact-checking explanations to explain predictions by comparing existing explanations against desirable properties to find out what makes for good explanations. |
| Outcome: | The proposed explanations are compared against desirable properties and show how they may lead to improvements in the research area. |
Document-level Claim Extraction and Decontextualisation for Fact-Checking (2024.acl-long)
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| Challenge: | Existing methods for document-level claim extraction focus on identifying and extracting claims from individual sentences. |
| Approach: | They propose a method for document-level claim extraction for fact-checking which aims to extract check-worthy claims from documents and decontextualise them so they can be understood out of context. |
| Outcome: | The proposed method extracts check-worthy claims from documents and decontextualises them so they can be understood out of context. |