# Article Reranking by Memory-Enhanced Key Sentence Matching for Detecting Previously Fact-Checked Claims

arXiv: https://arxiv.org/abs/2112.10322 · HTML: https://mlsanity.com/paper/2112.10322/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 58 references that are in the map.
- First posted on arXiv 20 Dec 2021.

## Builds on (most cited first, 23 of 23)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination](https://mlsanity.com/paper/1805.01978/) (2018)
- [Unsupervised Data Augmentation for Consistency Training](https://mlsanity.com/paper/1904.12848/) (2019)
- [Passage Re-ranking with BERT](https://mlsanity.com/paper/1901.04085/) (2019)
- [Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://mlsanity.com/paper/2004.09813/) (2020)
- [Multi-Stage Document Ranking with BERT](https://mlsanity.com/paper/1910.14424/) (2019)
- [The Fact Extraction and VERification (FEVER) Shared Task](https://mlsanity.com/paper/1811.10971/) (2018)
- [DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learning](https://mlsanity.com/paper/1809.06416/) (2018)
- [FANG: Leveraging Social Context for Fake News Detection Using Graph Representation](https://mlsanity.com/paper/2008.07939/) (2020)
- [That is a Known Lie: Detecting Previously Fact-Checked Claims](https://mlsanity.com/paper/2005.06058/) (2020)
- [Analogical Reasoning on Chinese Morphological and Semantic Relations](https://mlsanity.com/paper/1805.06504/) (2018)
- [Exploiting Multi-domain Visual Information for Fake News Detection](https://mlsanity.com/paper/1908.04472/) (2019)
- [Where Are the Facts? Searching for Fact-checked Information to Alleviate the Spread of Fake News](https://mlsanity.com/paper/2010.03159/) (2020)
- [Simple Applications of BERT for Ad Hoc Document Retrieval](https://mlsanity.com/paper/1903.10972/) (2019)
- [Complementing Lexical Retrieval with Semantic Residual Embedding](https://mlsanity.com/paper/2004.13969/) (2020)
- [Towards Better UD Parsing: Deep Contextualized Word Embeddings, Ensemble, and Treebank Concatenation](https://mlsanity.com/paper/1807.03121/) (2018)
- [Learning from Fact-checkers: Analysis and Generation of Fact-checking Language](https://mlsanity.com/paper/1910.02202/) (2019)
- [A Kernel of Truth: Determining Rumor Veracity on Twitter by Diffusion Pattern Alone](https://mlsanity.com/paper/2002.00850/) (2020)

## Built on by (most cited first, 16 of 16)

- [Generalizing to the Future: Mitigating Entity Bias in Fake News Detection](https://mlsanity.com/paper/2204.09484/) (2022)
- [FKA-Owl: Advancing Multimodal Fake News Detection through Knowledge-Augmented LVLMs](https://mlsanity.com/paper/2403.01988/) (2024)
- [Zoom Out and Observe: News Environment Perception for Fake News Detection](https://mlsanity.com/paper/2203.10885/) (2022)
- [Multilingual Previously Fact-Checked Claim Retrieval](https://mlsanity.com/paper/2305.07991/) (2023)
- [Memory-Guided Multi-View Multi-Domain Fake News Detection](https://mlsanity.com/paper/2206.12808/) (2022)
- [Combating Online Misinformation Videos: Characterization, Detection, and Future Directions](https://mlsanity.com/paper/2302.03242/) (2023)
- [MCFEND: A Multi-source Benchmark Dataset for Chinese Fake News Detection](https://mlsanity.com/paper/2403.09092/) (2024)
- [Characterizing Multi-Domain False News and Underlying User Effects on Chinese Weibo](https://mlsanity.com/paper/2205.03068/) (2022)
- [Autonomation, Not Automation: Activities and Needs of European Fact-checkers as a Basis for Designing Human-Centered AI Systems](https://mlsanity.com/paper/2211.12143/) (2022)
- [Breaking Language Barriers with MMTweets: Advancing Cross-Lingual Debunked Narrative Retrieval for Fact-Checking](https://mlsanity.com/paper/2308.05680/) (2023)
- [A Survey on Automatic Credibility Assessment Using Textual Credibility Signals in the Era of Large Language Models](https://mlsanity.com/paper/2410.21360/) (2024)
- [When Claims Evolve: Evaluating and Enhancing the Robustness of Embedding Models Against Misinformation Edits](https://mlsanity.com/paper/2503.03417/) (2025)
- [Towards Robust and Realible Multimodal Misinformation Recognition with Incomplete Modality](https://mlsanity.com/paper/2510.05839/) (2025)
- [The Psychology of Falsehood: A Human-Centric Survey of Misinformation Detection](https://mlsanity.com/paper/2509.15896/) (2025)
- [Mining the Explainability and Generalization: Fact Verification Based on Self-Instruction](https://mlsanity.com/paper/2405.12579/) (2024)
- [MultiMind at SemEval-2025 Task 7: Crosslingual Fact-Checked Claim Retrieval via Multi-Source Alignment](https://mlsanity.com/paper/2512.20950/) (2025)

## Abstract (arXiv)

False claims that have been previously fact-checked can still spread on social media. To mitigate their continual spread, detecting previously fact-checked claims is indispensable. Given a claim, existing works focus on providing evidence for detection by reranking candidate fact-checking articles (FC-articles) retrieved by BM25. However, these performances may be limited because they ignore the following characteristics of FC-articles: (1) claims are often quoted to describe the checked events, providing lexical information besides semantics; (2) sentence templates to introduce or debunk claims are common across articles, providing pattern information. Models that ignore the two aspects only leverage semantic relevance and may be misled by sentences that describe similar but irrelevant events. In this paper, we propose a novel reranker, MTM (Memory-enhanced Transformers for Matching) to rank FC-articles using key sentences selected with event (lexical and semantic) and pattern information. For event information, we propose a ROUGE-guided Transformer which is finetuned with regression of ROUGE. For pattern information, we generate pattern vectors for matching with sentences. By fusing event and pattern information, we select key sentences to represent an article and then predict if the article fact-checks the given claim using the claim, key sentences, and patterns. Experiments on two real-world datasets show that MTM outperforms existing methods. Human evaluation proves that MTM can capture key sentences for explanations. The code and the dataset are at https://github.com/ICTMCG/MTM.