Papers with dataset construction process
ToTTo: A Controlled Table-To-Text Generation Dataset (2020.emnlp-main)
Copied to clipboard
Ankur Parikh, Xuezhi Wang, Sebastian Gehrmann, Manaal Faruqui, Bhuwan Dhingra, Diyi Yang, Dipanjan Das
| Challenge: | Existing methods for data-to-text generation often hallucinate phrases not supported by the Wikipedia table. |
| Approach: | They propose a controlled task where annotators directly revise existing Wikipedia sentences to generate a one-sentence description. |
| Outcome: | The proposed task produces a one-sentence description from a Wikipedia table and highlighted cells. |
Chart-to-Text: A Large-Scale Benchmark for Chart Summarization (2022.acl-long)
Copied to clipboard
Shankar Kantharaj, Rixie Tiffany Leong, Xiang Lin, Ahmed Masry, Megh Thakkar, Enamul Hoque, Shafiq Joty
| Challenge: | Inferring key insights from charts can be challenging and time-consuming. |
| Approach: | They propose a task where the goal is to explain a chart and summarize key takeaways from it in natural language. |
| Outcome: | The proposed model produces fluent summaries but suffers from hallucinations and factual errors . the proposed model is compared with other models and can be used to generate BLEU scores . |
FinEntity: Entity-level Sentiment Classification for Financial Texts (2023.emnlp-main)
Copied to clipboard
| Challenge: | FinEntity annotates financial entity spans and their sentiment (positive, neutral, and negative) in financial news. |
| Approach: | They introduce an entity-level sentiment classification dataset called FinEntity that annotates financial entity spans and their sentiment in financial news. |
| Outcome: | The proposed dataset annotates financial entity spans and their sentiment (positive, neutral, and negative) in financial news. |