Text Summary Evaluation Task

A Text Summary Evaluation Task is a NLG evaluation task that involves assessing the quality of automatically or manually generated text summaries.

• Context:
  • input: a Text Summary, Text Summary Evaluation Measure.
  • output: a Text Summary Evaluation Score.
  • It can (typically) employ Summary Evaluation Metrics such as ROUGE, BLEU, and Flesch-Kincaid to measure quality aspects like conciseness, relevance, and readability.
  • It can range from being a Simple Text Summary Evaluation Task to being a Complex Text Summary Evaluation Task.
  • It can range from being a Human-Performed Text Summary Evaluation Task to being an Automated Text Summary Evaluation Task.
  • It can incorporate User Feedback to adjust and improve the evaluation criteria and the summarization algorithms themselves.
  • ...
• Example(s):
  • A system that uses ROUGE to evaluate summaries of scientific articles to aid researchers in literature review.
  • An online news portal that assesses summaries of daily news using both BLEU and user feedback to optimize content delivery to its audience.
  • a Contract Summary Evaluation Task, such as a redlined contract summary evaluation task.
  • ...
• Counter-Example(s):
  • Text Generation Tasks, which focus on creating text rather than evaluating it.
  • Document Classification Tasks, where the primary goal is to categorize documents into predefined topics.
• See: Natural Language Processing, Automated Text Summarization, Evaluation Metric

---

---

References

2024

• (Shakil et al., 2024) ⇒ Hassan Shakil, Atqiya Munawara Mahi, Phuoc Nguyen, Zeydy Ortiz, and Mamoun T Mardini. (2024). “Evaluating Text Summaries Generated by Large Language Models Using OpenAI's GPT.” In: arXiv preprint arXiv:2405.04053. doi:10.48550/arXiv.2405.04053
  • NOTES: The paper employs both traditional summary evaluation metrics (ROUGE, Latent Semantic Analysis, Flesch-Kincaid) and GPT-based evaluation to provide a comprehensive assessment of summary quality.

---