Summary of Metasumperceiver: Multimodal Multi-document Evidence Summarization For Fact-checking, by Ting-chih Chen et al.
MetaSumPerceiver: Multimodal Multi-Document Evidence Summarization for Fact-Checking
by Ting-Chih Chen, Chia-Wei Tang, Chris Thomas
First submitted to arxiv on: 18 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a summarization model designed to generate summaries of real-world claims from multimodal, multi-document datasets, aiming to facilitate fact-checking tasks. The model, based on a dynamic perceiver architecture, can handle inputs from multiple modalities of arbitrary lengths and is trained using a novel reinforcement learning-based entailment objective. The approach outperforms the state-of-the-art (SOTA) method by 4.6% in claim verification on the MOCHEG dataset and demonstrates strong performance on the Multi-News-Fact-Checking dataset. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us check if what people say is true or not. It’s like taking notes, but instead of writing them down, a computer program does it for you. This program looks at lots of different types of documents, like news articles and pictures, to help figure out if someone’s claim is true or false. |
Keywords
» Artificial intelligence » Reinforcement learning » Summarization
