# Interactive Text Ranking with Bayesian Optimisation: A Case Study on Community QA and Summarisation

arXiv: https://arxiv.org/abs/1911.10183 · HTML: https://mlsanity.com/paper/1911.10183/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Nov 2019.

## Built on by (most cited first, 5 of 5)

- [Checkpoint Merging via Bayesian Optimization in LLM Pretraining](https://mlsanity.com/paper/2403.19390/) (2024)
- [Uncertainty Quantification in Retrieval Augmented Question Answering](https://mlsanity.com/paper/2502.18108/) (2025)
- [Hone as You Read: A Practical Type of Interactive Summarization](https://mlsanity.com/paper/2105.02923/) (2021)
- [The Impact of Expertise in the Loop for Exploring Machine Rationality](https://mlsanity.com/paper/2302.05665/) (2023)
- [Rethinking Opinion Measurement Interfaces for Human-in-the-Loop Optimization](https://mlsanity.com/paper/2610.14757/) (2026)

## Abstract (arXiv)

For many NLP applications, such as question answering and summarisation, the goal is to select the best solution from a large space of candidates to meet a particular user's needs. To address the lack of user-specific training data, we propose an interactive text ranking approach that actively selects pairs of candidates, from which the user selects the best. Unlike previous strategies, which attempt to learn a ranking across the whole candidate space, our method employs Bayesian optimisation to focus the user's labelling effort on high quality candidates and integrates prior knowledge in a Bayesian manner to cope better with small data scenarios. We apply our method to community question answering (cQA) and extractive summarisation, finding that it significantly outperforms existing interactive approaches. We also show that the ranking function learned by our method is an effective reward function for reinforcement learning, which improves the state of the art for interactive summarisation.