Summary of A Comparative Study Of Quality Evaluation Methods For Text Summarization, by Huyen Nguyen et al.
A Comparative Study of Quality Evaluation Methods for Text Summarization
by Huyen Nguyen, Haihua Chen, Lavanya Pobbathi, Junhua Ding
First submitted to arxiv on: 30 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposed method utilizes large language models (LLMs) to evaluate text summarization, addressing the limitations of automatic metrics that heavily rely on reference summaries. The paper conducts a comparative study between eight automatic metrics, human evaluation, and the LLM-based approach, evaluating seven state-of-the-art summarization models on datasets with patent documents. The results show that LLMs align closely with human evaluation, while widely-used automatic metrics such as ROUGE-2, BERTScore, and SummaC lack consistency. Based on this empirical comparison, a LLM-powered framework is proposed for automatically evaluating and improving text summarization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how to evaluate text summaries. Right now, it’s tricky because we have to choose between using automatic tools or asking humans to help. The authors suggest a new way to do this using big language models. They test their idea by comparing it with other ways of evaluating summaries and show that it works well. This could be useful for people who want to make better summaries, like AI systems. |
Keywords
» Artificial intelligence » Rouge » Summarization
