Summary of Evaluating the Factuality Of Zero-shot Summarizers Across Varied Domains, by Sanjana Ramprasad et al.
Evaluating the Factuality of Zero-shot Summarizers Across Varied Domains
by Sanjana Ramprasad, Kundan Krishna, Zachary C Lipton, Byron C Wallace
First submitted to arxiv on: 5 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A recent study on large language models (LLMs) found that zero-shot generated summaries of news articles are comparable or even preferred to manually composed reference summaries. However, this research focused primarily on news article summarization. This new work evaluates the performance of zero-shot generated summaries in various specialized domains, including biomedical articles and legal bills, while focusing on factuality and errors. The study analyzes whether the prevalence of a domain in the pretraining corpus affects the extractiveness and faithfulness of generated summaries. The research releases all collected annotations to facilitate further investigation into measuring and achieving factually accurate summarization beyond news articles. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Zero-shot generated summaries can summarize articles without any prior training. Researchers have found that these summaries are often as good as those written by humans. But so far, most studies have only looked at summarizing news articles. This new study looks at summarizing different types of articles, such as medical and legal ones. The researchers want to know if the summaries are accurate and if they can be improved. They also release all their data so that others can use it to improve summary quality. |
Keywords
* Artificial intelligence * Pretraining * Summarization * Zero shot
