Create README.md

README.md

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+This dataset only consists of linearized underlying data table of charts and their corresponding summaries. 
+
+Model that use this dataset: https://huggingface.co/saadob12/t5_C2T_big
+## Created By: 
+Kanthara, S., Leong, R. T. K., Lin, X., Masry, A., Thakkar, M., Hoque, E., & Joty, S. (2022). Chart-to-Text: A Large-Scale Benchmark for Chart Summarization. arXiv preprint arXiv:2203.06486.
+
+**Paper**: https://arxiv.org/abs/2203.06486
+
+**Orignal github repo**: https://github.com/vis-nlp/Chart-to-text
+
+# Abstract from the Paper
+Charts are commonly used for exploring data
+and communicating insights. Generating nat-
+ural language summaries from charts can be
+very helpful for people in inferring key in-
+sights that would otherwise require a lot of
+cognitive and perceptual efforts. We present
+Chart-to-text, a large-scale benchmark with
+two datasets and a total of 44,096 charts cover-
+ing a wide range of topics and chart types. We
+explain the dataset construction process and
+analyze the datasets. We also introduce a num-
+ber of state-of-the-art neural models as base-
+lines that utilize image captioning and data-to-
+text generation techniques to tackle two prob-
+lem variations: one assumes the underlying
+data table of the chart is available while the
+other needs to extract data from chart images.
+Our analysis with automatic and human eval-
+uation shows that while our best models usu-
+ally generate fluent summaries and yield rea-
+sonable BLEU scores, they also suffer from
+hallucinations and factual errors as well as dif-
+ficulties in correctly explaining complex pat-
+terns and trends in charts.
+
+### Note
+The original paper published two sub-datasets one collected from statista and the other from pew. The dataset upload here is from statista. Images can be downloaded from the github repo mentioned above. 
+
+# Dataset split
+| train | valid  | test |
+|:---:|:---:| :---:|
+| 24367  | 5222  | 5222 |