REVIEW 4 cited by
Chart-to-Text: Generating Natural Language Descriptions for Charts by Adapting the Transformer Model
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
Information visualizations such as bar charts and line charts are very popular for exploring data and communicating insights. Interpreting and making sense of such visualizations can be challenging for some people, such as those who are visually impaired or have low visualization literacy. In this work, we introduce a new dataset and present a neural model for automatically generating natural language summaries for charts. The generated summaries provide an interpretation of the chart and convey the key insights found within that chart. Our neural model is developed by extending the state-of-the-art model for the data-to-text generation task, which utilizes a transformer-based encoder-decoder architecture. We found that our approach outperforms the base model on a content selection metric by a wide margin (55.42% vs. 8.49%) and generates more informative, concise, and coherent summaries.
Forward citations
Cited by 4 Pith papers
-
Chart-to-Experience: Benchmarking Multimodal LLMs for Predicting Experiential Impact of Charts
Multimodal LLMs underperform humans at directly rating charts' experiential impact, but they are substantially better at pairwise comparisons, especially when the human ratings differ clearly.
-
Do Large Multimodal Models Solve Caption Generation for Scientific Figures? Lessons Learned from SciCap Challenge 2023
In human evaluations by three professional editors, GPT-4V captions for scientific figures were preferred over author-written captions and over captions from challenge-winning models.
-
ChartInsighter: An Approach for Mitigating Hallucination in Time-series Chart Summary Generation with A Benchmark Dataset
A multi-agent LLM pipeline with external computation and self-consistency checking produces time-series chart summaries with fewer annotated hallucinations than GPT-4 or VL2NL on the authors' new benchmark.
-
ChatVis: Large Language Model Agent for Generating Scientific Visualizations
A retrieval-augmented LLM assistant with iterative error correction nearly doubles the rate of generating executable ParaView visualization scripts compared with unassisted models.
Discussion (0). Continue with ORCID to comment.