Chart to text generation
For exploring data and sharing perspectives, the visualizations of information like the bar charts and line charts are always common. For certain individuals, such as those people who are blind or have poor visualization literacy or visually impaired, decoding and making sense of those visualizat...
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| Format: | Final Year Project |
| Language: | English |
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Nanyang Technological University
2021
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| Online Access: | https://hdl.handle.net/10356/147962 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | For exploring data and sharing perspectives, the visualizations of information like the bar
charts and line charts are always common. For certain individuals, such as those people who
are blind or have poor visualization literacy or visually impaired, decoding and making sense
of those visualizations would be much difficult. We present a new dataset and a neural model
for automatically generating natural language captions for charts in this paper. The captions
that are produced provide an overview of the chart and express the key insights found within
it. The data-to-text generation task utilize the state-of-the-art model, which uses a transformer based encoder-decoder architecture, was used to construct our neural model. We discovered
that our phase1 method outperforms the base model by a large margin on a content selection
metric (55.42 percent vs. 8.49 percent) and produces more informative, succinct, and coherent
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