# DeText: A Deep Text Ranking Framework with BERT

arXiv: https://arxiv.org/abs/2008.02460 · HTML: https://mlsanity.com/paper/2008.02460/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Aug 2020.

## Built on by (most cited first, 13 of 13)

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- [Improving Domain-Specific Retrieval by NLI Fine-Tuning](https://mlsanity.com/paper/2308.03103/) (2023)
- [Fine-tuning BERT with Bidirectional LSTM for Fine-grained Movie Reviews Sentiment Analysis](https://mlsanity.com/paper/2502.20682/) (2025)
- [Beyond Accuracy: An Empirical Study on Unit Testing in Open-source Deep Learning Projects](https://mlsanity.com/paper/2402.16546/) (2024)
- [OKRA: an Explainable, Heterogeneous, Multi-Stakeholder Job Recommender System](https://mlsanity.com/paper/2504.07108/) (2025)
- [MLPlatt: Simple Calibration Framework for Ranking Models](https://mlsanity.com/paper/2601.08345/) (2026)
- [Avey-B](https://mlsanity.com/paper/2602.15814/) (2026)

## Abstract (arXiv)

Ranking is the most important component in a search system. Mostsearch systems deal with large amounts of natural language data,hence an effective ranking system requires a deep understandingof text semantics. Recently, deep learning based natural languageprocessing (deep NLP) models have generated promising results onranking systems. BERT is one of the most successful models thatlearn contextual embedding, which has been applied to capturecomplex query-document relations for search ranking. However,this is generally done by exhaustively interacting each query wordwith each document word, which is inefficient for online servingin search product systems. In this paper, we investigate how tobuild an efficient BERT-based ranking model for industry use cases.The solution is further extended to a general ranking framework,DeText, that is open sourced and can be applied to various rankingproductions. Offline and online experiments of DeText on threereal-world search systems present significant improvement overstate-of-the-art approaches.