# An Exploration of Post-Editing Effectiveness in Text Summarization

**Authors:** Vivian Lai, Alison Smith-Renner, Ke Zhang, Ruijia Cheng, Wenjuan Zhang, Joel Tetreault, Alejandro Jaimes  
**arXiv:** [2206.06383](https://arxiv.org/abs/2206.06383)  
**Date:** 2022-06-13  
**Summary page:** https://www.summarizepaper.com/en/paper/2206.06383v1/

## Key points

- Trade-off between automatic summarization's efficiency and potential low quality versus manual summarization's higher quality but expensive nature
- Exploration of human-AI collaboration to enhance summarization performance
- Concept of "post-editing" AI-generated text by humans to reduce workload and improve output quality
- Experiment comparing post-edited summaries with manually generated ones across formal (XSum news) and informal (Reddit posts) texts
- Evaluation of summary quality, human efficiency, and user experience in both settings
- Effectiveness of post-editing in text summarization scenarios, especially when participants lacked domain knowledge
- Diminished utility of post-editing when dealing with inaccurate information in provided summaries
- Nuanced nature of human-AI collaboration in text summarization processes
- Potential advantages of post-editing for enhancing text summarization processes and optimizing future developments for improved performance and user satisfaction

## Summary

In their study titled "An Exploration of Post-Editing Effectiveness in Text Summarization," authors Vivian Lai, Alison Smith-Renner, Ke Zhang, Ruijia Cheng, Wenjuan Zhang, Joel Tetreault, and Alejandro Jaimes delve into the realm of text summarization methods. They highlight the trade-off between automatic summarization's efficiency and potential low quality versus manual summarization's higher quality but expensive nature. The researchers pose a critical question: can humans and AI collaborate to enhance summarization performance? Drawing parallels with similar text generation tasks like machine translation, the team explores the concept of "post-editing" AI-generated text by humans to reduce workload and improve output quality. Through an experiment involving 72 participants, they compare post-edited summaries with manually generated ones across formal (XSum news) and informal (Reddit posts) texts. The study aims to evaluate summary quality, human efficiency, and user experience in both settings. The findings shed light on the effectiveness of post-editing in text summarization scenarios. While it proved beneficial in cases where participants lacked domain knowledge, its utility diminished when dealing with inaccurate information in provided summaries. The diverse editing strategies employed by participants underscore the nuanced nature of human-AI collaboration in this context. Overall, this research offers valuable insights into the potential advantages of post-editing for enhancing text summarization processes. By understanding users' varying needs for assistance and refining collaborative approaches between humans and AI systems, future developments in human-AI summarization systems can be optimized for improved performance and user satisfaction.