Summary of A Systematic Review Of Data-to-text Nlg, by Chinonso Cynthia Osuji et al.
A Systematic Review of Data-to-Text NLG
by Chinonso Cynthia Osuji, Thiago Castro Ferreira, Brian Davis
First submitted to arxiv on: 13 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The systematic review examines the current state of research on data-to-text generation, identifying gaps and future directions. It reviews datasets, evaluation metrics, application areas, multilingualism, language models, and hallucination mitigation methods. The study explores various methods for producing high-quality text, including re-ranking, pipeline architecture, planning architectures, data cleaning, controlled generation, and model modifications. The effectiveness and limitations of these methods are assessed, highlighting the need for strategies to mitigate hallucinations. The review also discusses dataset usage, popularity, and impact, emphasizing both automatic and human assessment. Additionally, it explores the evolution of data-to-text models, particularly transformer models. Despite advancements in text quality, the review emphasizes the importance of research in low-resourced languages and dataset engineering. It highlights several application domains, emphasizing their relevance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Data-to-text generation is a way to turn data into natural-sounding text. This review looks at what we know so far about this process. It’s like doing a big puzzle – taking many different pieces of information and fitting them together to make sense. The study finds that there are some problems, like “hallucinations” where the computer makes things up that aren’t true. To fix these issues, researchers use different methods, such as cleaning up the data or using special language models. The review also talks about how we evaluate this process and what kinds of languages it can work with. It’s an important area because it could be used in many different fields, like helping people understand complex information. |
Keywords
* Artificial intelligence * Hallucination * Text generation * Transformer
