# Technology Assisted Reviews: Finding the Last Few Relevant Documents by Asking Yes/No Questions to Reviewers

**Authors:** Jie Zou, Dan Li, Evangelos Kanoulas  
**arXiv:** [1810.05414](https://arxiv.org/abs/1810.05414)  
**Date:** 2018-10-12  
**Summary page:** https://www.summarizepaper.com/en/paper/1810.05414v1/

## Key points

- Authors: Jie Zou, Dan Li, and Evangelos Kanoulas
- Title of the paper: "Technology Assisted Reviews: Finding the Last Few Relevant Documents by Asking Yes/No Questions to Reviewers"
- Problem addressed: Achieving high recall with low human effort in technology-assisted reviews
- Proposed approach: Sequential Bayesian search method for efficiently identifying and retrieving crucial relevant documents with minimal manual reviewing effort
- Benefits of the methodology: Enhancing technology-assisted reviews by effectively utilizing entities within documents and valuable insights from reviewers
- Experimental results: Demonstrated significant improvements in efficiency and effectiveness compared to traditional methods

## Summary

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In their paper titled "Technology Assisted Reviews: Finding the Last Few Relevant Documents by Asking Yes/No Questions to Reviewers," authors Jie Zou, Dan Li, and Evangelos Kanoulas address the challenge of achieving high recall with low human effort in technology-assisted reviews. Their proposed approach leverages a sequential Bayesian search method to efficiently identify and retrieve the last few crucial relevant documents with minimal manual reviewing effort. This innovative methodology offers a promising solution for enhancing technology-assisted reviews by effectively utilizing entities within documents and valuable insights from reviewers. The authors' experimental results demonstrate significant improvements in efficiency and effectiveness compared to traditional methods.