Tabular data is used to store information in many real-world systems ranging from finance to healthcare. However, such structured data is often communicated to humans in visually interpretable formats (e.g. charts and textual paragraphs), making it imperative that fact-checking models should be able to reason over multiple pieces of structured evidence presented across different modalities. In this paper, we propose Multi-Document Multi-Modal Table-based Fact Verification (M²-TabFact), a challenging fact verification task that requires jointly reasoning over visual and textual representations of structured data. We design an automatic data generation pipeline that converts existing tabular data into descriptive visual and textual evidence. We then use Large Language Models to generate complex claims that depend on multi-document, multi-modal evidence. In total, we create 8,856 pairs of complex claims and multi-modal evidence through this procedure and systematically evaluate M²-TabFact with a set of strong vision-language models (VLM). We find that existing VLMs have large gaps in fact verification performance compared to humans. Moreover, we find that they are imbalanced when it comes to their ability to handle reason about different modalities, and currently struggle to reason about information extracted from multiple documents.

Advances in large vision-language models (LVLMs) have led to significant progress in generating natural language descriptions for visual contents. These powerful models are known for producing texts that are factually inconsistent with the visual input. While some efforts mitigate such inconsistencies in natural image captioning, the factuality of generated captions for structured visuals, such as charts, has not received as much scrutiny. This work introduces a comprehensive typology of factual errors in generated chart captions. A large-scale human annotation effort provides insight into the error patterns in captions generated by various models, ultimately forming the foundation of a dataset, CHOCOLATE. Our analysis reveals that even advanced models like GPT-4V frequently produce captions laced with factual inaccuracies. To combat this, we establish the task of Chart Caption Factual Error Correction and introduce CHARTVE, a visual entailment model that outperforms current LVLMs in evaluating caption factuality. Furthermore, we propose C2TFEC, an interpretable two-stage framework that excels at correcting factual errors. This work inaugurates a new domain in factual error correction for chart captions, presenting a novel evaluation metric, and demonstrating an effective approach to ensuring the factuality of generated chart captions. The code and data as well as the continuously updated benchmark can be found at: https://khuangaf.github.io/CHOCOLATE/.

Transcripts of natural, multi-person meetings differ significantly from documents like news articles, which can make Natural Language Generation models for generating summaries unfocused. We develop an abstractive meeting summarizer from both videos and audios of meeting recordings. Specifically, we propose a multi-modal hierarchical attention across three levels: segment, utterance and word. To narrow down the focus into topically-relevant segments, we jointly model topic segmentation and summarization. In addition to traditional text features, we introduce new multi-modal features derived from visual focus of attention, based on the assumption that the utterance is more important if the speaker receives more attention. Experiments show that our model significantly outperforms the state-of-the-art with both BLEU and ROUGE measures.